Early versions of OCR had to be trained with images of each character and could only work with one font at a time. Modern machine learning algorithms make the text recognition process more advanced and provide a higher level of recognition accuracy for most fonts, regardless of input data formats. Advances in machine learning (ML) have given a new impetus to the development of OCR, significantly increasing the number of its applications. With enough training data, the OCR machine learning algorithm now can be applied to any real-world scenario that requires identification and text transformation. For example, receipts scanning, scanning of printed text with the further conversion of it into synthetic speech, traffic sign recognition, license plate recognition, etc.
We explained the 5 Ws of artificial intelligence in developing countries. In recent years, artificial intelligence hasn't had a very favorable reputation overall. It is considered a threat to human employment opportunities even though we use artificial intelligence in everyday life. Is artificial intelligence better than human intelligence? The answer to this question will differ from person to person, but there is something that cannot be denied.
The past month has seen a frenzy of articles, interviews, and other types of media coverage about Blake Lemoine, a Google engineer who told The Washington Post that LaMDA, a large language model created for conversations with users, is "sentient." After reading a dozen different takes on the topic, I have to say that the media has become (a bit) disillusioned with the hype surrounding current AI technology. A lot of the articles discussed why deep neural networks are not "sentient" or "conscious." This is an improvement in comparison to a few years ago, when news outlets were creating sensational stories about AI systems inventing their own language, taking over every job, and accelerating toward artificial general intelligence. But the fact that we're discussing sentience and consciousness again underlines an important point: We are at a point where our AI systems--namely large language models--are becoming increasingly convincing while still suffering from fundamental flaws that have been pointed out by scientists on different occasions.
Artificial intelligence will be the common theme in the top 10 technology trends in the next few years, and these are expected to quicken breakthroughs across key economic sectors and society, the Alibaba Damo Academy says. The global research arm of Chinese technology major Alibaba Group says innovation will be extended from the physical world to a mixed reality, as more innovation finds its way to industrial applications and digital technology drives a green and sustainable future. "Digital technologies are growing faster than ever," Jeff Zhang, president of Alibaba Cloud Intelligence and head of Alibaba Damo, said in a report released on Monday. "The advancements in digitisation, 'internetisation' and intelligence are redefining a digital world that is characterised by the prevalence of mixed reality. "Digital technology plays an important role in powering a green and sustainable future, whether it is applied in industries such as green data centres and energy-efficient manufacturing, or in day-to-day activities like paperless office."
Nick Bostrom is a Swedish-born philosopher and polymath with a background in theoretical physics, computational neuroscience, logic, and artificial intelligence, as well as philosophy. He is a Professor at Oxford University, where he leads the Future of Humanity Institute as its founding director. He is the author of some 200 publications, including Anthropic Bias (2002), Global Catastrophic Risks (2008), Human Enhancement (2009), and Superintelligence: Paths, Dangers, Strategies (2014), a New York Times bestseller which helped spark a global conversation about artificial intelligence. Bostrom's widely influential work, which traverses philosophy, science, ethics, and technology, has illuminated the links between our present actions and long-term global outcomes, thereby casting a new light on the human condition. He is recipient of a Eugene R. Gannon Award, and has been listed on Foreign Policy's Top 100 Global Thinkers list twice.
Osteoarthritis is a common medical condition. Unfortunately, despite the support of X-ray imaging technology in diagnosis, the accuracy of diagnostic results still depends on human factors. Furthermore, when errors do occur, they are often detected late, leading to a waste of time, money, and even disability for the patient. This study has deployed and evaluated transfer learning techniques in abnormal and normal bone images classification on X-ray images collected from the dataset of MUsculoskeletal RAdiographs (MURA) with 17,367 images and then leveraged techniques for results explanations of learning algorithms such as Gradient-weighted Class Activation Mapping (GRAD-CAM) to provide visual highlighted interesting areas in the images which can be signals for anomalies in bones. The classification performance using MobileNet with techniques of hyper-parameters fine-tuning can reach an accuracy of 0.84 in abnormal and normal bone classification tasks on the wrist, humerus, and elbow.
ML from scratch is a student-led tutorial / seminar series initiated by Johannes Bill and others from Jan Drugowitsch Lab at Harvard Medical School. The objective is to teach neuroscience students to learn cutting edge machine learning models by implementing them. I started participating from 2022, and I prepared the tutorial and led a few seminars in it!
As machine learning-based models continue to be developed for healthcare applications, greater effort is needed in ensuring that these technologies do not reflect or exacerbate any unwanted or discriminatory biases that may be present in the data. In this study, we introduce a reinforcement learning framework capable of mitigating biases that may have been acquired during data collection. In particular, we evaluated our model for the task of rapidly predicting COVID-19 for patients presenting to hospital emergency departments, and aimed to mitigate any site-specific (hospital) and ethnicity-based biases present in the data. Using a specialized reward function and training procedure, we show that our method achieves clinically-effective screening performances, while significantly improving outcome fairness compared to current benchmarks and state-of-the-art machine learning methods. We performed external validation across three independent hospitals, and additionally tested our method on a patient ICU discharge status task, demonstrating model generalizability.