"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Welcome to part two of the Food AI series of papers! Last week, I wrote an article explaining the seminal im2recipe paper that introduced cross-modality into dealing with machine learning related to food applications such as searching for the correct recipe using a photo, automatically determining the number of calories in a dish or improving the performance of various recipe recommendation and ranking systems. I refer the reader to that article for an introduction and motivation to the problem and for details regarding the Recipe1M dataset and evaluation metrics. As mentioned in the previous article, these explanations aim at charting the progress of research in a particular domain of machine learning. So, today, we will be looking at the paper titled "Dividing and Conquering Cross-Modal Recipe Retrieval: from Nearest Neighbours Baselines to SoTA" published in 2019.
Almost a year ago to the day, the Center for Research on Foundation Models (CRFM) – which was then a new initiative of the Stanford Institute for Human-Centered Artificial Intelligence (HAI) – held a virtual workshop on Foundation Models. They chose "Foundation" as the name for these models as they entail training one model on a huge amount of data, then adapting it to many applications. The data can be text, images, speech, and more. The tasks that such models can perform include – but are not limited to – answering questions, analyzing sentiments, extracting information form text, labeling images, and recognizing objects. These Foundation Models use self-supervised learning, and they work because they can effectively apply knowledge learned in one task to another task.
According to ARRS' American Journal of Roentgenology (AJR), machine learning models applied to presently underutilized imaging features could help construct more reliable criteria for organ allocation and liver transplant eligibility. "The findings suggest that machine learning-based models can predict recurrence before therapy allocation in patients with early-stage hepatocellular carcinoma (HCC) initially eligible for liver transplant," wrote corresponding author Julius Chapiro from the department of radiology and biomedical imaging at Yale University School of Medicine in New Haven, CT. Chapiro and colleagues' proof-of-concept study included 120 patients (88 men, 32 women; median age, 60 years) diagnosed with early-stage HCC between June 2005 and March 2018, who were initially eligible for liver transplant and underwent treatment by transplant, resection, or thermal ablation. Patients underwent pretreatment MRI and posttreatment imaging surveillance, and imaging features were extracted from postcontrast phases of pretreatment MRI examinations using a pretrained convolutional neural network (VGG-16). Pretreatment clinical characteristics (including laboratory data) and extracted imaging features were integrated to develop three ML models--clinical, imaging, combined--for recurrence prediction within 1–6 years posttreatment. Ultimately, all three models predicted posttreatment recurrence for early-stage HCC from pretreatment clinical (AUC 0.60–0.78,
The concept of machine learning is a development in the field of artificial intelligence. Many field experts say that artificial intelligence is the future for mankind because it can help in many ways. The term machine learning comes from the 1950s which was introduced by the most famous mathematician, namely Alan Turing. He was the inventor of the first digital computer. His contribution and that of other computer scientists are highly appreciated by the world because of his enormous contribution to the world of technology.
Artificial intelligence (AI) is the collective term for computer technologies and techniques that help solve complex problems by imitating the brain's ability to learn. AI helps computers recognize patterns hidden within a lot of information, solve problems and adjust to changes in processes as they happen, much faster than humans can. Researchers use AI to be better and faster at tackling the most difficult problems in science, medicine and technology, and help drive discovery in those areas. This could range from helping us understand how COVID-19 attacks the human body to finding ways to manage traffic jams. Many Department of Energy (DOE) facilities, like Argonne National Laboratory, assist in developing some the most advanced AI technologies available.
It's safe to say that Plato and his contemporaries never grappled with moral questions raised by the development of neural networks or issues surrounding data privacy and security. But a few modern philosophers – like Kathleen Creel – are doing just that as they harness age-old ideas about knowledge, existence, and ethics to understand and respond to the challenges posed by today's technology. "I still get a lot from Plato and other historical philosophers, but the task of philosophy is to figure out what the questions of a particular age, of a particular society or culture are, and to ask how philosophy can help to address them," Creel says. "It gives us a clearer moral system to help sort through what our priorities ought to be, and how we should act in our lives." Creel is finishing a two-year Embedded EthiCS Postdoctoral Fellowship based at Stanford's McCoy Family Center for Ethics in Society and the Institute for Human-Centered Artificial Intelligence (HAI).
You may have heard ETL getting thrown in sentences here and there when you're reading blogs or watching YouTube videos. So what does ETL have to do with machine learning? For those who don't already know, machine learning is a type of artificial intelligence that uses data analysis to predict accurate outcomes. It is the machine learning algorithms that produce these predicted outputs by learning on historical data and its features. It is the process of moving data from multiple sources to bring it to a centralized single database.
Description: Conversational AI has seen tremendous progress in recent years, achieving near-human or even surpassing human performance in certain well-defined tasks, including speech recognition and question answering. Yet it tends to struggle with tasks which are less constrained, in particular those that involve producing human language. Current approaches to natural language generation (NLG) in dialogue systems still heavily rely on techniques that lack scalability and transferability to different domains, despite the general embrace of more robust methods by the NLG community, in particular deep learning (neural) models. These methods rely on large amounts of annotated data, yet they tend to produce generic, robotic, and boring responses that lack most of the human language nuances that make conversation creative and varied. While the naturalness of the generated language is an important factor affecting the perceived quality of a dialogue system, semantic accuracy is also extremely important.
QuantumBlack, AI by McKinsey recently sat down with Selim Turki, head of data and AI at Careem, to discuss the latest trends in advanced analytics and artificial intelligence. Far from a dry discussion of theory, the conversation coalesced around several fascinating use cases in which Careem is using AI to make a difference in people's lives. We discussed how AI is being leveraged to improve customer and driver security through targeted facial-recognition checks to ensure drivers (captains) are who they say they are. We also discussed how AI is being used to provide customers with the most accurate and up-to-date estimated times of arrival (ETAs) by factoring in a host of conditions, including local weather conditions, prayer times, and even iftar times during Ramadan. Along the way, we discussed what it means to be an "AI first" company and the outlook for AI tech--and talent--in the region.
Google's DeepMind neural network has demonstrated that it can dream up short videos from a single image frame, and it's really cool to see how it works. As DeepMind noted on Twitter, the artificial intelligence model, named "Transframer" -- that's a riff on a "transformer," a common type of AI tool that whips up text based on partial prompts -- "excels in video prediction and view synthesis," and is able to "generate 30 [second] videos from a single image." Transframer is a general-purpose generative framework that can handle many image and video tasks in a probabilistic setting. New work shows it excels in video prediction and view synthesis, and can generate 30s videos from a single image: https://t.co/wX3nrrYEEa As the Transframer website notes, the AI makes its perspective videos by predicting the target images' surroundings with "context images" -- in short, by correctly guessing what one of the chairs below would look like from different perspectives based on extensive training data that lets it "imagine" an actual object from another angle.