This blog is a contribution from our customer University Medical Centre Mannheim, a leading university hospital in Europe. Learn how their team leverages DataRobot to accelerate clinical research with AI.
As physicians and researchers, we’re constantly working to improve quality of life. To do so, we need a holistic, data-driven approach that helps us understand the full impact of a particular treatment. How does a certain treatment impact patient symptoms? What makes their symptoms better or worse?
At University Medical Centre Mannheim, we’re digging into data to answer these questions. But it’s tough for our small teams to balance research with the critically important treatment of our patients. We don’t learn data science in medical school, but it’s an increasingly essential piece of the healthcare puzzle.
Fortunately, user-friendly AI platforms like DataRobot are helping us bridge the gaps between our medical expertise and the data science we need to provide more thoughtful care to our patients.
What excites me most about AI in healthcare is the potential to uncover new explanations for diseases or breakthrough therapy efficacies that we’re too blind to see using classical statistical methods. Our goal is to uncover new influences on disease progression, predict disease flares, and empower patients to better manage their treatment adherence.
DataRobot gives us exciting new ways to gain insights from our data and augment our team without data scientists.
As clinicians, we can compare and validate models to find those with the highest degree of accuracy. In the Clinical Cooperation Unit – Healthy Skin and Joints, we’ve leveraged AI to evaluate data from a smartphone app, including images and other clinical datasets of anonymized patient data.
Compliance is also critical — from privacy measures around patient information to GDPR regulations that protect and secure sensitive data. When we publish our findings, the most important thing is their reproducibility. That’s why documentation and explainability behind models are so critical. DataRobot makes these normally labor-intensive processes seamless and automatic.
With DataRobot, we trust our findings, knowing that they have been thoroughly trained, retrained, validated, and revalidated. We have a plethora of statistics to show the level of accuracy, which we also need for publishing results. Because of that, I sleep better at night and our centre can make an even greater contribution to the medical research community.
Another example of AI being used in healthcare: we’ve applied AI to several use cases in our dermatology and rheumatology collaboration.
For a recently published study, we used DataRobot to analyze data from clinical research with patients with chronic eczema or psoriasis. The analysis focused on itching, pain, quality of life, and the use of a smartphone monitoring app to track their symptoms. We looked at uncovering new influences on disease progression, trying to predict disease flares or promote patient treatment adherence.
Through our analysis, we learned that nearly 30% of patients see improved quality of life at six months, while another 30% either showed a decline in quality of life or had consistently poor quality of life. Those insights and others will influence treatment decisions. This data is transformative because we can better understand our patients and learn which patients benefit from certain therapies. It informs us on when and how to change patients’ course of treatment if needed.
Now we’re helping other clinics in the medical center uncover insights in their data. With the Department of Internal Medicine, we’ve looked at blood lipids with the goal of predicting heart disease or heart attacks. In just a few weeks/months, we’ve been able to create some pretty accurate models and look forward to publishing our findings in the near future.
All these findings may have gone undiscovered without DataRobot. Instead, we’ve been able to accelerate research from hours to seconds, even as we continue to see patients and focus on improving their quality of life
AI helps our daily work, and most importantly, it helps patients.
When we first partnered with DataRobot, I told others that this new technology would change the face of the Earth and that they had to learn about it. I’m still saying this today. AI offers enormous benefits to healthcare professionals, and I’m thrilled to see the impact of University Medical Centre Mannheim’s work.
The post Customer Spotlight: How Doctors and Researchers Optimize Patient Outcomes with AI appeared first on DataRobot.
The robotics industry stands on the brink of a significant transformation, with many experts – including NVIDIA CEO Jensen Huang – suggesting that we might be approaching a “ChatGPT moment” for robotics.
At the core of this revolution is the use of neural networks to create versatile robotic “brains” that enable robots to tackle various tasks much like humans do. Additionally, it seems that major players in the field have opted to build “humanoids,” designing their robots to mimic human form and size. The reasoning behind this approach is both simple and profound: our world is inherently designed for humans. From tools to vehicles to architectural spaces, nearly everything around us is built with human dimensions and capabilities in mind. Therefore, developing humanoid robots that can seamlessly navigate and operate within this human-centric environment is a logical and efficient strategy.
Recent breakthroughs in imitation learning, combined with the power of generative AI, are accelerating the pace of innovation. Imitation learning allows robots to learn complex tasks by observing human actions, while generative AI enhances the training process by creating vast amounts of synthetic data. Moreover, the decreasing cost of hardware components has removed one of the significant barriers to entry, making it more feasible to develop sophisticated robotic systems.
In this article, we will delve deeper into these favorable factors driving the progress in humanoid robotics. We will also explore the ongoing challenges that need to be addressed, provide an overview of the major players in this space, and discuss the prospects for the widespread adoption of humanoid robots.
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The rapid advancements in humanoid robotics are being driven by several favorable factors, each contributing to a landscape ripe with opportunity. From the decreasing costs of hardware to the innovative application of AI in building robotic brains, these developments are not only accelerating research but also making the widespread deployment of humanoid robots increasingly feasible. Below, we explore four key opportunities shaping the future of humanoid robotics.
One of the most significant drivers of progress in humanoid robotics is the decreasing cost of essential components. The price of manufacturing humanoid robots has dropped considerably, making advanced robotics research more accessible to a broader range of institutions and companies. Just a year ago, the cost of producing a humanoid robot ranged from $50,000 to $250,000 per unit. Today, that range has narrowed to between $30,000 and $150,000.
The integration of AI, particularly generative AI, into robotics has shifted the focus from mere physical dexterity to the development of sophisticated “robot brains.” These neural networks function similarly to the human brain, controlling various aspects of the robot’s behavior and allowing it to adapt to different scenarios and tasks. Unlike traditional robotics, which required painstakingly detailed programming and training, AI-powered robots can learn and adjust on the go. This adaptability is a game-changer, enabling robots to perform a wider variety of tasks with increased competence and autonomy, thus expanding their potential applications across industries.
Imitation learning, a technique where robots learn by mimicking human actions, has gained renewed attention in the robotics community. This method involves using virtual reality or teleoperation to teach robots complex tasks by example, a process that is proving particularly effective in manipulation tasks. The resurgence of this technique is largely due to its compatibility with the latest AI advancements, particularly in generative AI. By leveraging imitation learning, researchers can extend the principles of AI beyond text, images, and video into the realm of robot movement, opening up new possibilities for teaching robots a broad range of skills in a more intuitive and efficient manner.
One of the longstanding challenges in robotics has been the scarcity of high-quality training data. Generative AI offers a powerful solution to this problem by creating vast amounts of synthetic data that can be used to train robots. With the ability to generate relevant visual scenarios and other forms of data, AI enables researchers to simulate a wide variety of environments and situations, thereby providing robots with the diverse experiences needed to learn new skills.
While these opportunities are driving significant progress in humanoid robotics, there remain critical challenges that need to be addressed to fully unlock the potential of this technology. Let’s explore these in the next section.
While the progress in humanoid robotics is promising, several significant challenges remain that must be addressed to achieve widespread adoption and integration. These challenges span technical, economic, and ethical domains, highlighting the complexity of developing and deploying humanoid robots at scale. Below, we outline seven key challenges currently facing the field.
Despite recent reductions in components costs, humanoid robots remain expensive, posing a barrier to mass adoption and commercialization. The development and ongoing maintenance of these advanced systems require substantial financial investment. For many potential users, especially in smaller industries or research institutions, the cost of acquiring and maintaining humanoid robots is still prohibitively high.
Bipedal robots are notoriously energy-intensive, requiring efficient power systems and advanced energy management to operate effectively. The high energy demands limit the runtime of these robots, restricting their usefulness in many applications. Although advancements in battery technology offer potential solutions, current battery life of up to 5 hours still falls short of what is needed for extended, continuous operation.
The production of humanoid robots is also constrained by the limited availability of certain critical components. High-precision components, such as those requiring specialized grinding machines, are difficult to source in large quantities due to limited industrial capacity or long manufacturing cycle times. This bottleneck not only keeps costs high but also hinders the ability to scale production to meet potential demand.
Effective human-robot interaction remains a challenging area, particularly when it comes to natural language processing and intuitive command interpretation. For instance, enabling robots to reliably take voice commands from a person without prior training is a significant hurdle. Developing more sophisticated AI systems that can understand and respond to a wide range of human inputs, including nuanced voice commands, is vital for making robots more user-friendly and accessible in everyday environments.
One of the technical challenges that continue to limit the functionality of humanoid robots is their ability to perform precise control and coordination tasks. For example, while Figure 02 boasts 16 degrees of freedom in its hands, this is still far less than the 27 degrees of freedom found in a human hand. This limitation affects the robot’s ability to perform delicate and complex tasks, such as grasping and manipulating objects.
Humanoid robots rely heavily on cameras and sensors to perceive their environment, which can limit their understanding and responsiveness. These sensory systems, while advanced, still fall short of the human ability to intuitively understand and interact with complex, dynamic environments.
As humanoid robots become more integrated into society, legal and ethical considerations are increasingly coming to the forefront. Questions around liability, privacy, and the potential displacement of human workers are significant concerns that need to be addressed. Moreover, developing regulations that govern the lawful and ethical use of robots will require interdisciplinary collaboration among technologists, ethicists, and policymakers. Ensuring that the advancement of humanoid robots is responsible and aligned with societal values is essential for their long-term acceptance and success.
Despite the significance of these challenges, they are not insurmountable. With continued innovation and collaboration across the industry, these obstacles can be addressed, paving the way for humanoid robots to become a common presence in both commercial and everyday settings. Several major players are already competing to build the first truly mass-adoptable humanoid robots, each pushing the boundaries of what’s possible. In the next section, we will take a closer look at these key companies and their contributions to the future of humanoid robotics.
In the rapidly evolving field of humanoid robotics, several companies are emerging as key players, each contributing uniquely to the development and potential commercialization of these advanced machines. In this section, we will take a closer look at four leading companies: Figure, Tesla, Agility Robotics, and 1X. These innovators are at the forefront of creating robots designed to integrate seamlessly into human environments, and their advancements are shaping the future of humanoid robotics.
Figure is an innovative AI robotics company with a bold mission to develop general-purpose autonomous humanoid robots that can support human activities on a global scale. Their robots are equipped with advanced speech-to-speech reasoning capabilities, powered by embedded ChatGPT technology, which allows them to interact more naturally and effectively with humans. Figure’s latest model, Figure 02, is touted as the world’s first commercially viable autonomous humanoid robot, designed to provide valuable support in industries such as manufacturing, logistics, warehousing, and retail.
The company has made significant strides in both technology and business, raising $854 million in funding, with their latest Series B round bringing the company’s valuation to $2.6 billion. Figure’s impressive list of investors includes major players like Microsoft, OpenAI Startup Fund, NVIDIA, Bezos Expeditions, Intel Capital, and ARK Invest. These backers clearly see potential in Figure’s ability to lead the commercialization and widespread deployment of humanoid robots, setting the company apart as a key player in the robotics industry.
Optimus, developed by Tesla, is a general-purpose, bipedal, humanoid robot that can perform tasks deemed dangerous, repetitive, or boring for humans. The latest model of Optimus boasts impressive capabilities, including advanced bipedal locomotion, dexterous hands for delicate object manipulation, and improved balance and full-body control. Optimus is designed to perform tasks such as lifting objects, handling tools, and potentially working in environments like factories and warehouses.
Elon Musk announced that Tesla plans to begin “limited production” of the Optimus robot in 2025, with initial testing of these humanoid robots taking place in Tesla’s own factories starting next year. He anticipates that by 2025, Tesla could have “over 1,000, or even a few thousand” Optimus robots operational within the company.
Agility Robotics focuses on developing versatile bipedal robots designed to navigate and work within human environments. Their flagship robot, Digit, is engineered to perform tasks that require mobility and dexterity, such as moving objects in tight or complex spaces. The latest model of Digit is equipped with advanced sensors, agile limbs, and robust software that allows it to navigate obstacles and interact with its surroundings efficiently. Digit’s capabilities were put to the test in a real-world scenario at a Spanx factory, marking its first significant job deployment.
Agility Robotics has attracted considerable financial backing, raising nearly $180 million from prominent investors, including DCVC, Playground Global, and Amazon. This funding supports Agility Robotics’ ongoing efforts to refine Digit’s capabilities and scale production, positioning the company as a key player in the future of humanoid robotics.
1X is a robotics company focused on creating humanoid robots designed to seamlessly integrate into various environments, from commercial settings to home use. They have introduced Eve, a humanoid robot aimed at working alongside commercial teams in sectors like logistics and security. Eve is capable of taking on tasks that require both physical dexterity and cognitive reasoning, making it a valuable asset in these industries. In addition to Eve, 1X is developing Neo, an intelligent humanoid assistant designed to assist people in their homes, performing a wide range of domestic tasks. Both Eve and Neo can respond to simple voice commands without the need for complex prompts. They will intelligently break down complex requests into manageable steps, ensuring that tasks are completed efficiently and effectively.
1X has garnered significant attention and financial support, raising $136 million from a range of high-profile investors, including EQT Ventures, OpenAI, Samsung Next, Tiger Global, and others. This funding supports their mission to advance the development of humanoid robots that can work closely with humans in both commercial and personal settings.
The adoption of humanoid robots is anticipated to grow significantly over the coming decades, with projections suggesting a substantial impact across various industries. According to Goldman Sachs, the total addressable market for humanoid robots is expected to reach $38 billion by 2035. This growth is largely driven by the potential demand in structured environments such as manufacturing, where robots can be employed for tasks like electric vehicle assembly and component sorting. The appeal of humanoid robots lies in their ability to take on jobs that are considered “dangerous, dirty, and dull,” making them ideal candidates for roles in mining, disaster rescue, nuclear reactor maintenance, and chemicals manufacturing. In these sectors, the willingness to pay a premium for robots capable of performing hazardous tasks is particularly high.
Similarly, Morgan Stanley’s research outlines a tiered approach to the adoption of humanoid robots across different industries. They predict that robots will initially be adopted in industries characterized by boring, repetitive, or dangerous tasks. Morgan Stanley categorizes these industries into three tiers: Tier 1 includes sectors such as forestry, farming, food preparation, and personal care, where adoption is expected to begin around 2028. Tier 2, which includes sales, transportation, and more specialized healthcare jobs, is projected to see adoption by 2036. Finally, Tier 3, encompassing areas like arts, design, entertainment, sports, and media, is anticipated to integrate humanoid robots by 2040.
In summary, the future of humanoid robotics is bright, with the potential to revolutionize how we approach tasks in both commercial and personal settings. As these technologies continue to mature, we can expect humanoid robots to become an integral part of our daily lives, performing tasks that were once thought to be the exclusive domain of humans.
We’ll let you know when we release more summary articles like this one.
The post Humanoid Robots on the Rise: Industry Advances, Key Players, and Adoption Timelines appeared first on TOPBOTS.
