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AI Breakthroughs in Endoscopy
- AI has vast potential in the medical field, particularly in procedures like endoscopy that require complex analysis and expert insight. Early uses of AI in endoscopy have already shown promising results by improving detection and classification, streamlining procedures, lowering cross-contamination risks, and expanding specialist training. However, AI comes with a few drawbacks, such as skewed training data that may cause AI to amplify human biases. Medical organizations must build safer AI policies to mitigate the negative effects while capitalizing on the benefits.
- The most significant AI breakthrough in endoscopy is how machine learning improves detection with an accuracy as high as 96.4%. AI models could detect small abnormalities like precancerous polyps or lesions more accurately than humans. AI models can complement specialists to give greater confidence in their diagnoses without a time-consuming process and help health care systems can provide patients with early help, leading to improved outcomes.
- Machine vision models are also adept at classification, making sure that people get the care they need by detecting subtle differences in abnormal growths. With AI models that distinguish between colorectal polyps with up to 87% accuracy, doctors can diagnose patients more accurately and provide quicker treatments with improved outcomes.
- Endoscopy AI is fast on top of being accurate and specific. Quicker AI detection and classification without taking additional time lead to improved patient outcomes with earlier treatment and let a constrained workforce serve a larger number of patients, making turnover and labor shortages less impactful.
- AI can help in ensuring cleaner, safer storage and sanitization. Smart drying cabinets with HEPA filtration, positive pressurization, and similar steps dry and disinfect endoscopes between procedures. Algorithms monitor interior conditions in real time and adjust settings as necessary to maintain sterile storage as cabinets open and close. Predicting equipment failures can alert staff to compromise endoscope cleanliness, thus preventing infections and improving overall health.
- AI is a useful training tool that can show trainees what various polyps, lesions, or other abnormalities look like and can quickly improve their detection and classification skills. With AI streamlining specialist training, reliable endoscopy and related care will become accessible to more people, working against long-standing barriers to care between different demographics.
- However, using AI carelessly can cause it to amplify human biases and introduce privacy concerns. Medical organizations must recognize these downsides and build safer AI policies that mitigate the negative effects while capitalizing on the benefits. A diverse team must oversee AI development and frequently audit the algorithm to find and correct biased tendencies while using synthetic data to protect patient privacy while providing a larger training database. Health care systems must train doctors to use AI carefully by emphasizing that human experts should always have the final say and teaching professionals about AI’s shortcomings to prevent them from over-relying on the technology.
- AI in endoscopy will likely expand in adoption and reshape the field. These procedures will become more accurate, precise, accessible, efficient, and safe as trends continue, benefiting both doctors and patients.
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The Robot Report
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Mcity says open-source digital twin enables cheaper autonomous vehicle testing
- The University of Michigan's Mcity Test Facility has released the first open-source digital twin, allowing researchers worldwide to access a free tool for testing autonomous and connected vehicle software.
- The digital twin provides a virtual representation of the physical Mcity Test Facility and enables simulation and testing without the need for researchers to travel to Ann Arbor, Michigan.
- Developed with support from the National Science Foundation, the digital twin offers a faster, safer, and less expensive way to test algorithms for autonomous and connected vehicles.
- The digital twin works with the open-source traffic simulator TeraSim, allowing simulation of various road scenarios, including potential collisions and interactions with pedestrians, cyclists, and other drivers.
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Siliconangle
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The robots are coming! And Nvidia is going to power them with a revamped Jetson and lower price
- Nvidia has released the Jetson Orin Nano Super Developer Kit.
- The artificial intelligence (AI) computer offers up to 67 tera-operations per second of AI performance.
- It is a diminutive four inches by three and a half inches by 1.3 inches, costs $249 and the company said it redefines AI for small edge devices.
- The developer kit features 8GB, six-core Arm central processing unit and an Ampere architecture graphics processing unit, and supported software includes frameworks for robotics, vision AI, and sensor processing.
- The kit comes with the Jetson Orin Nano module and a reference carrier for accommodating Orin Nano and Nvidia Orin NX modules.
- Available a year and a half after its $499 predecessor, a free software upgrade for all Jetson Orin users turns their devices into the new super version.
- Nvidia General Manager of Robotics and Edge Computing, Deepu Talla, calls the Jetson Orin Nano Super Developer Kit the most affordable and powerful supercomputer the company's built.
- Nvidia designed the Jetson architecture to support faster performance to permit what it calls 'planned rejuvenation', rather than 'planned obsolescence.'
- Both the new kit and upgrades are available from the Nvidia website.
- In the call announcing the device, Talla mentioned that the serviceable market for robots was approximately $50 trillion.
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The Robot Report
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Waymo robotaxis head to Tokyo with the help of Nihan Kotsu and GO
- Waymo is bringing its autonomous vehicles to Tokyo for its first international "road trip" in partnership with Nihan Kotsu and GO.
- The first all-electric Jaguar I-PACEs for Waymo will arrive in Tokyo in early 2025, with Nihon Kotsu overseeing the management and servicing of the vehicles.
- Initially, Nihon Kotsu drivers will operate the vehicles manually to map key areas of Tokyo, allowing the Waymo Driver to learn and adapt to left-hand traffic and new driving nuances.
- Waymo's expansion into Japan aligns with the country's goals for transportation and the company is engaging with local officials to ensure a responsible implementation of self-driving cars.
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Siliconangle
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Nvidia launches Jetson Orin Nano Super, a powerful AI brain for robotics and edge
- Nvidia has launched the Jetson Orin Nano Super, a compact AI developer kit.
- The Jetson Orin Nano Super offers powerful robotics and edge AI compute at a low cost.
- It delivers a 1.7-times leap in generative AI inference performance and a 50% increase in memory bandwidth.
- The developer kit is now priced at $249, making it more accessible to developers, students, and engineers.
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The Robot Report
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Realbotix earns Amazon development subsidy; partners with UOL
- Realbotix Corp. secures a $100,000 subsidy from Amazon Web Services to support the development of its AI-powered humanoid robots.
- Realbotix partners with Compass UOL to develop a web-based robot controller with advanced animations, AI integration, and local processing capabilities.
- Realbotix's Aria robotic assistant will provide insight to the company's board of directors and act as a third-party advisor.
- Realbotix utilizes advanced AI models and techniques, such as Chat GPT-4 and retrieval augmented generation (RAG), in its technology development.
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Unite
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How Neurosymbolic AI Can Fix Generative AI’s Reliability Issues
- Generative AI is powerful but often fails to get facts accurate and relies on probabilities to make decisions, making it unreliable, particularly in fields like healthcare, law, or finance.
- Neurosymbolic AI can solve reliability issues faced by generative AI by combining the strengths of neuronal networks with symbolic logic, increasing and verifying generated information's accuracy.
- Neurosymbolic AI can bring transparency to generative AI and show exactly how the system arrived at an answer, building trust and increasing the AI's reliability.
- GraphRAG is a case study that shows the potential of combining the strengths of neurosymbolic and generative AI by organizing information into nodes using knowledge graphs and cross-referencing answers with trusted information.
- Combining neurosymbolic AI with generative AI is challenging and requires a balance between creativity and accuracy, which isn’t always easy to achieve.
- Future directions include developing hybrid systems that can switch between the two methods depending on the task's requirements and improving the trackability of the AI's reasoning to increase trust and confidence.
- Neurosymbolic AI addresses the unpredictability and lack of understanding of generative AI, making AI decision-making creative yet dependable.
- As AI continues to evolve, neurosymbolic AI offers a path forward, one where we can count on the answers AI provides, even when lives and livelihoods are at stake.
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Unite
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How AI Will Keep People Fed Amid Agriculture’s Turmoil
- As AI disrupts nearly every industry, the agriculture sector is embracing machine learning, computer vision, and other data-driven processes for efficient farming. The agriculture industry faces a long, diverse list of problems and disruptions including labor issues, war, and climate change that will further inflate food insecurity figures if not quickly corrected. The first step in supplementing shrinking profits and meet global demand is for tech providers to build trust with growers, which can happen by highlighting where AI is already making a massive difference.
- Critical roles like scouting, harvesting, and managing irrigation systems require expert knowledge and training to be performed acceptably, and emerging technological tools aim to simplify and enhance these tasks. The US is investing in AI and robotics to help aging growers to boost the productivity of labor-intensive tasks like picking and plowing while providing data-driven insights to make informed decisions. AI can provide relevant ammunition in the battle to boost food security.
- The Netherlands have embraced AI in agriculture to implement precision farming practices and leverage computer vision to monitor plant health, and make data-driven decisions in farms and greenhouses. The country is investing in crop varieties that can be more resilient to climate change and less dependent on chemical crop protection. Many small-scale, local growers who do not have the resources to absorb the impact of pandemics, wars, or climate change are also embracing AI's applications.
- In many industries, AI applications are more theoretical and need time to undergo testing and quality assurance. Healthcare, a prime example of an industry that needs AI help, is limited by concerns surrounding data privacy and malpractice. However, in agriculture, we are seeing more farms and farmers empowered by new AI applications. AI is helping growers make agriculture a more viable business endeavor, more sustainable, and more efficient overall.
- The UN estimates that farmers must produce 70% more food to feed the predicted global population of 9.1 billion people in 2050. With rising input costs, shrinking production values, and shifting markets, and the added pressure from climate change, farmer's profit margins continue to decline resulting in food insecurity worldwide. Sophisticated AI tools and advancements in technology can provide farmers with a better understanding of their yield and crops. However, replacing skilled workers who can perform critical roles like scouting, harvesting, and managing irrigation systems is not as simple.
- Data sharing and collaborations between growers and tech providers can help spread valuable information that boosts productivity and crop knowledge, enabling AI systems to improve while allowing growers to gain valuable insights. AI and data sharing can even help alert farming communities of new crop threats spreading in a specific region. Rising food insecurity will ultimately lead to economic hardships, conflicts, and widespread destabilization affecting all aspects of humanity. Expanding and spreading AI's positive impact on agriculture can help tackle the problem.
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Unite
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Steve Herne, CEO at Unlearn – Interview Series
- Steve Herne is the CEO at Unlearn, a company that develops AI technologies for clinical development and precision medicine. The company's focus is on digital twins, powered by its Unlearn Platform, which enable it to build virtual patient models and streamline clinical development. Herne, a former CCO, says his focus now is on aligning the company's strategic goals, vision and mission, as well as building a commercially driven business model. Herne is challenging his team to understand the voice of customers, so they can address trial and error in medicine more effectively.
- Unlearn's goal is to reduce error and decrease the number of trial participants. The company has already integrated digital twin technology into clinical trials, identitying some potential advantages, including a reduced timeline compared to the traditional trial process. Herne believes that digital twin technology has the potential to enhance every clinical trial, and that every pharmaceutical company will adopt it in protocols over the next decade.
- Herne is focused on strategic growth for the company, fosterin a culture of innovation while maintaining commercial viability of the AI solutions being developed. He wants to see clinicians viewing Unlearn as a partner of choice for creating digital twins in their clinical trials.
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Unite
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AI and Financial Crime Prevention: Why Banks Need a Balanced Approach
- Financial criminals are leveraging AI technology to produce deepfake videos, voices and fake documents that can get past computer and human detection.
- Generative AI is expected to accelerate fraud losses to an annual growth rate of 32% in the US to reach US$40 billion by 2027 according to Deloitte.
- Banks are deploying AI in anti-financial crime (AFC) efforts to monitor transactions, generate suspicious activity reports, automate fraud detection.
- AI-driven systems introduce a complex “black box” element due to opaque decision-making processes.
- Banks need careful planning, thorough testing, specialized compliance frameworks and human oversight to ensure AI accountability.
- Human judgment remains essential in AFC investigations and AI systems require Explainable AI tools to make AI-driven decisions understandable to regulators.
- Financial institutions can combine a rules-based approach with AI tools to create a multi-layered system that leverages the strengths of both approaches.
- Risk and compliance experts must be trained in AI to develop compliance frameworks specific to AI.
- High-quality, secure data infrastructure is essential for AI implementation.
- AI is a two-sided coin for banks that needs a cautious, hybrid approach to minimize risks and increase efficiency.
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Unite
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Ofer Haviv, President and CEO of Evogene – Interview Series
- Ofer Haviv is the CEO and President of Evogene, a computational biology company specializing in transforming product discovery and development across various life science industries, including human health and agriculture.
- Evogene’s core technology is its versatile Computational Predictive Biology (CPB) platform, which uses unique technological engines that integrate data, computational systems, and a deep understanding of life sciences to drive innovation across different fields.
- Evogene’s long-term goals are to invest in their technology engines, expand the variety of their engine’s usage and promote the value of their subsidiaries.
- The core principle behind Evogene's CPB platform with its AI tech-engines is that it integrates a deep understanding of biology and chemistry with AI, machine learning, computational models, and biological data to perform analyses across millions of data points.
- Evogene attracts top-tier talent in biology, AI, and data science by fostering a collaborative environment that integrates multidisciplinary experience and creativity in their team to tackle complex challenges with innovative solutions.
- Evogene's collaboration with Google Cloud is a partnership that has driven advanced AI technologies to transform small molecule drug discovery and development.
- The foundation model is a cutting-edge innovation in drug and product development, enabling pre-training on significantly larger datasets than traditional AI-methods which also allows for getting deeper insights.
- Evogene’s competitive edge stems from the expertise of its multidisciplinary team, agility in tailoring solutions to market needs, and proprietary algorithms to enhance screening and optimization.
- Evogene envisions expanding their technological reach through strategic partnerships to shape the future of life sciences product development.
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The Robot Report
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Eyeonic Trace Laser Line Scanner offers sub-millimeter depth perception
- SiLC Technologies has launched Eyeonic Trace, a laser line scanner that can provide sub-millimeter depth precision for next-generation warehouse automation, robotics, farming, construction and manufacturing applications.
- The scanner is compact with a wide 72-degree field of view which allows for the measurement of large objects and is positioned two to three metres above a conveyor belt to capture big boxes while still accurately upholding millimetre precision.
- Eyeonic Trace is also unaffected by ambient light and can provide millimetre precision from as far as 10 meters away. It’s designed to provide better depth precision than the human eye, reducing the need for human workers in applications that may have an impact on their health.
- The scanner uses a highly-integrated silicon photonics chip to allow the Eyeonic Trace to accurately dimension objects in dynamic ranges while accommodating objects with diverse reflective properties and functioning reliably indoors or outdoors.
- SiLC Technologies is showcasing the Eyeonic Trace Laser Line Scanner at CES in Las Vegas this month which is the company’s first turnkey, fully integrated offering with samples expected to be available in Q2 2025.
- The firm has already gained investment from Honda Xcelerator Ventures and the Eyeonic Trace Laser Line Scanner is designed to provide the next level of AI-driven automation and mobility.
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Alleywatch
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Cofactr Raises $17.2M to Help Aerospace and Defense Companies Navigate Complex Supply Chains
- Cofactr has raised $17.2m in a funding round led by Bain Capital Ventures.
- Cofactr is streamlining the supply chains of high-compliance companies including those in aerospace, defense and medical technology.
- The logistics management platform streamlines parts sourcing and supplier procurement, providing access to a network of pre-vetted suppliers.
- Part of the platform's remit is to help organisations maintain compliance while speeding product development cycles.
- The platform works by creating a seamless link between Product Lifecycle Management, Enterprise Resource Planning and Manufacturing Execution Systems.
- Cofactr is the only end-to-end electronics procurement and logistics solution, letting companies streamline supply chain management.
- The platform serves more than 50 customers, spanning high-compliance sectors such as defence and regulated industries, as well as consumer-facing industries like wearables and autonomous vehicles.
- Cofactr's SaaS platform offers a modular option at affordable prices that lets smaller teams start with the basics and expand as they grow.
- The company plans to debut several new platform capabilities in the next six months, including an AI-powered source-to-quote module and improved approval and collaboration tools.
- The firm plans to expand its capabilities and launch additional product categories and modules.
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The Robot Report
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Image Credit: The Robot Report
Slip Robotics picks up $28M for trailer loading/unloading robots
- Slip Robotics has raised $28 million in Series B funding for its SlipBots, which automate and accelerate truck loading and unloading.
- Truckers spend 23% of their workday idling at loading docks, and SlipBots can cut loading times from 30 to 60 minutes to just five.
- Companies like John Deere, GE Appliances, Nissan, and Valeo are already using SlipBots in their production operations.
- The funding will help Slip Robotics expand its team, increase deployments, and enhance its product offerings.
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