Modern artificial intelligence uses complex algorithms to perform all kinds of tasks in an instant, for example to determine the feeling of a client based on his or her examination or to identify specific features of an image . However, the brightest moments of artificial intelligence come from the creativity with which we use these algorithms. People used the AI for generate new sports, transform scribbles into realistic landscapesand now, MIT has found a way to detect breast cancer up to five years in advance using an in-depth image classification model.
The MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Massachusetts General Hospital (MGH) have used mammograms and known results from more than 60,000 patients to form their new model to the smallest visual detail from the human eye. Well-trained doctors do not miss these predictive patterns simply because they may seem too small to be noticed, but because more subtle patterns just do not attract enough attention. An image classification model that can categorize thousands of scans down to the smallest detail can quickly solve this daunting task.
Regina Barzilay, a professor at MIT (and breast cancer survivor), explains how this new model can improve treatment plans:
Rather than taking a single approach, we can customize the screening for cancer risk in women. For example, a doctor might recommend to a group of women to have a mammogram every two years, while another higher-risk group could undergo additional MRI screening.
When doctors can order mammograms according to the needs of the patient, they can avoid unnecessary exposure to radiation and the cost of potentially unnecessary examinations. Although existing models can accurately identify 18% of patients in the high-risk category, this new model increases this number by up to 31%. Its success is based on the team's approach to its development. For the first time, a model of breast cancer prevention targets women individually. It also takes into account racial diversity, where earlier models focused mainly on white populations. This not only contributes to increased accuracy, but significantly reduces the breast cancer mortality rate among African-American women.
As demonstrated by MIT and HGM, well-trained image classification models can help doctors save lives. Although no AI gives perfect results, image classification algorithms have matured and become reliable in many applications, especially in specific models such as this one. You need a little more than a good idea, relevant data and a little time to create a successful image recognition model. Services like Clarifai, Microsoft Azure, IBM Watson, Vize and others offer free, bespoke customized training platforms that require no programming skills. Thanks to these algorithms, accessible to all, we have all the necessary resources to train AI to solve problems and help others. It takes time and care to safely integrate a successful experience into the practice of diagnostic medicine; This approach will likely see many revisions as it develops outside of a single hospital. But the first results are promising.
The robots outnumber men in many areas, but the human hand is always superior. Robots have difficulty picking up irregular objects. Now, a new forceps designed at MIT's Computer and Artificial Intelligence Laboratory (CSAIL) could give robots an edge. The origami gripper "magic ball" does not try to imitate our hands, but it can still hold 100 times its weight.
The origami gripper is an application of what is commonly called "soft robotics". As their name suggests, these machines use flexible materials like rubber instead of cold and inflexible metal. The new CSAIL device looks like a soft paper flower, but it contracts to grab objects with incredible force. It does not matter what these objects are as long as you can stall some of them inside the cone.
The gripper consists of three parts: a folding internal skeleton, a rubber exoskeleton and the flexible connector. It works by pumping gas into and out of the tight interior space. This simple construction allows the team to quickly and efficiently change the design to improve functionality in various contexts.
Companies like Amazon are extremely interested in developing robots that can recover irregular objects, and soft robotics is probably the best way to do it. Currently, the team states that the tapered gripper can hold almost any item with a diameter of 70% or less and up to 100 times its weight. It is also much sweeter than a traditional robot arm, capable of lifting both heavy and delicate objects like glass bottles and foods. CSAIL has tested the Magic Ball Clamp with various objects such as water, fruit, aluminum cans, etc. This robot could almost Unpack your errands without scrambling the eggs.
The Magic Ball Clamp is currently ideal for cylindrical objects such as bottles and cans, but it can lift irregular objects as long as it fits into the cone opening. Large flat objects, like books, are difficult. Humans also use the forceps in the demonstration above, which is necessary because of objects that are too large in some dimensions. The team plans to add a computer vision to the gripper, which would allow it to find the right angle to grab an object.