While covering advanced topics, this article is accessible to professionals with limited knowledge in statistical or mathematical theory. It introduces new material not covered in my recent book (available here) on applied stochastic processes. You don't need to read my book to understand this article, but the book is a nice complement and introduction to the concepts discussed here. None of the material presented here is covered in standard textbooks on stochastic processes or dynamical systems. In particular, it has nothing to do with the classical logistic map or Brownian motions, though the systems investigated here exhibit very similar behaviors and are related to the classical models.
Last June, a team at Harvard Medical School and MIT showed that it's pretty darn easy to fool an artificial intelligence system analyzing medical images. Researchers modified a few pixels in eye images, skin photos and chest X-rays to trick deep learning systems into confidently classifying perfectly benign images as malignant. These so-called "adversarial attacks" implement small, carefully designed changes to data--in this case pixel changes imperceptible to human vision--to nudge an algorithm to make a mistake. That's not great news at a time when medical AI systems are just reaching the clinic, with the first AI-based medical device approved in April and AI systems besting doctors at diagnosis across healthcare sectors. Now, in collaboration with a Harvard lawyer and ethicist, the same team is out with an article in the journal Science to offer suggestions about when and how the medical industry might intervene against adversarial attacks.
These days, pundits galore are proselytizing about the Future of Work. Depending on who you ask, the robots may or may not be taking over, leaving us mere humans pondering how work fits into our lives and whether we're going to be eventually rendered obsolete. Just look at the stark contrast in tone between these two headlines: the Wall Street Journal's White-Collar Robots Are Coming For Jobs versus Wired's Chill: Robots Won't Take All Our Jobs. Who should we *really* believe?! The truth is there isn't one easy answer.
Medical artificial intelligence breaks a little too easily. Although AI promises to improve healthcare by quickly analysing medical scans, there is increasing evidence that it trips up on seemingly innocuous changes. Sam Finlayson at Harvard Medical School and his colleagues fooled three AIs designed for scanning medical images into misclassifying them by simply altering a few pixels. In one example, the team ever so slightly altered a picture of a mole that was first classified as benign with 99 per cent confidence. The AI then classified the altered image as malignant with 100 per cent confidence, despite the two images being indistinguishable to the human eye.
With any change comes the fear of the unknown, but this is especially true when it comes to artificial intelligence. Universities today have so much to gain by leveraging AI across the student lifecycle, but many are hesitant. Taking a step back, this somewhat nebulous concept of AI is already taking root in our everyday lives in so many forms. Today, you can wake up with a reminder and a playlist of your favorite motivational morning music via a voice-activated assistant, then get traffic advice on your way to work from a maps app. A quick tap on a suggestion based on previous purchases, and your favorite variety of coffee is waiting at your favorite store, already paid for in-app.
I use simulation of two multilayer neural networks to gain intuition into the determinants of human learning. The first network, the teacher, is trained to achieve a high accuracy in handwritten digit recognition. The second network, the student, learns to reproduce the output of the first network. I show that learning from the teacher is more effective than learning from the data under the appropriate degree of regularization. Regularization allows the teacher to distinguish the trends and to deliver "big ideas" to the student. I also model other learning situations such as expert and novice teachers, high- and low-ability students and biased learning experience due to, e.g., poverty and trauma. The results from computer simulation accord remarkably well with finding of the modern psychological literature. The code is written in MATLAB and will be publicly available from the author's web page.
Sagami Koyokan High School, in the city of Zama about 40 km southwest of Tokyo, has a unique entrance exam system, provides Japanese classes of various levels, uses a system of team teaching and employs a coordinator to help students from overseas or of foreign descent enroll and better understand classes in Japanese. Foreign students or students with foreign backgrounds make up some 20 percent of a student body that totals more than 1,000. The origins and backgrounds of students at Sagami Koyokan can be traced to more than a dozen countries, including China, the Philippines, Vietnam, Peru and Brazil, and the number of those with roots in South Asian nations such as Nepal and Sri Lanka has been on the rise recently, according to deputy head Kumiko Sakakibara. "I find it easier to ask questions in this (special Japanese) class with fewer students because I feel nervous in a large classroom," said a 19-year-old Sri Lankan student named Adhil. One of his classmates, Manalo Dominic Piedad, 18, from the Philippines, said: "I came to understand more (in Japanese) compared to before. At this school, I made a lot of friends from various countries."
Federated learning obtains a central model on the server by aggregating models trained locally on clients. As a result, federated learning does not require clients to upload their data to the server, thereby preserving the data privacy of the clients. One challenge in federated learning is to reduce the client-server communication since the end devices typically have very limited communication bandwidth. This paper presents an enhanced federated learning technique by proposing a synchronous learning strategy on the clients and a temporally weighted aggregation of the local models on the server. In the asynchronous learning strategy, different layers of the deep neural networks are categorized into shallow and deeps layers and the parameters of the deep layers are updated less frequently than those of the shallow layers. Furthermore, a temporally weighted aggregation strategy is introduced on the server to make use of the previously trained local models, thereby enhancing the accuracy and convergence of the central model. The proposed algorithm is empirically on two datasets with different deep neural networks. Our results demonstrate that the proposed asynchronous federated deep learning outperforms the baseline algorithm both in terms of communication cost and model accuracy.
Access to skilled workers is already a key factor that sets successful companies apart from failing ones. In an increasingly data-driven future - the European Commission believes there could be as many as 756,000 unfilled jobs in the European ICT sector by 2020 - this difference will become even more acute. Skills gaps across all industries are poised to grow in the Fourth Industrial Revolution. Rapid advances in artificial intelligence (AI), robotics and other emerging technologies are happening in ever shorter cycles, changing the very nature of the jobs that need to be done - and the skills needed to do them - faster than ever before. At least 133 million new roles generated as a result of the new division of labour between humans, machines and algorithms may emerge globally by 2022, according to the World Economic Forum.
That's the new reality for many classrooms across Abu Dhabi, where a company is using artificial intelligence to create a new learning experience. Established four years ago, Alef Education has managed to get its digital education platform into dozens of schools in Abu Dhabi, as well as Al Ain, another city in the emirate. Alef has worked closely with the government of the United Arab Emirates to bring the platform to 25,000 students at 57 public schools. It is also used in two private schools in Abu Dhabi. And the startup has made its first move into the United States, where its technology is used in two private schools in New York.