The successive subspace learning (SSL) principle was developed and used to design an interpretable learning model, known as the PixelHop method,for image classification in our prior work. Here, we propose an improved PixelHop method and call it PixelHop++. First, to make the PixelHop model size smaller, we decouple a joint spatial-spectral input tensor to multiple spatial tensors (one for each spectral component) under the spatial-spectral separability assumption and perform the Saab transform in a channel-wise manner, called the channel-wise (c/w) Saab transform.Second, by performing this operation from one hop to another successively, we construct a channel-decomposed feature tree whose leaf nodes contain features of one dimension (1D). Third, these 1D features are ranked according to their cross-entropy values, which allows us to select a subset of discriminant features for image classification. In PixelHop++, one can control the learning model size of fine-granularity,offering a flexible tradeoff between the model size and the classification performance. We demonstrate the flexibility of PixelHop++ on MNIST, Fashion MNIST, and CIFAR-10 three datasets.

Cyber-physical system (CPS) has operated, controlled and coordinated the physical systems integrated by a computing and communication core applied in industry 4.0. To accommodate CPS services, fog radio and optical networks (F-RON) has become an important supporting physical cyber infrastructure taking advantage of both the inherent ubiquity of wireless technology and the large capacity of optical networks. However, cyber security is the biggest issue in CPS scenario as there is a tradeoff between security control and privacy exposure in F-RON. To deal with this issue, we propose a brain-like based distributed control security (BLCS) architecture for F-RON in CPS, by introducing a brain-like security (BLS) scheme. BLCS can accomplish the secure cross-domain control among tripartite controllers verification in the scenario of decentralized F-RON for distributed computing and communications, which has no need to disclose the private information of each domain against cyber-attacks. BLS utilizes parts of information to perform control identification through relation network and deep learning of behavior library. The functional modules of BLCS architecture are illustrated including various controllers and brain-like knowledge base. The interworking procedures in distributed control security modes based on BLS are described. The overall feasibility and efficiency of architecture are experimentally verified on the software defined network testbed in terms of average mistrust rate, path provisioning latency, packet loss probability and blocking probability. The emulation results are obtained and dissected based on the testbed.

The White House is pushing to dedicate an additional billion dollars to fund artificial intelligence research, effectively doubling the budget for that purpose outside of Defense Department spending, Reuters reported today, citing people briefed on the plan. Investment in quantum computing would also receive a major boost. The 2021 budget proposal would reportedly increase AI R&D funding to nearly $2 billion, and quantum to about $860 million, over the next two years. The U.S. is engaged in what some describe as a "race" with China in the field of AI, though unlike most races this one has no real finish line. Instead, any serious lead means opportunities in business and military applications that may grow to become the next globe-spanning monopoly, a la Google or Facebook -- which themselves, as quasi-sovereign powers, invest heavily in the field for their own purposes.

Probably the worst error is thinking there is a correlation when that correlation is purely artificial. Take a data set with 100,000 variables, say with 10 observations. You are almost guaranteed to find one above 0.999. This is best illustrated in may article How to Lie with P-values (also discussing how to handle and fix it.) This is being done on such a large scale, I think it is probably the main cause of fake news, and the impact is disastrous on people who take for granted what they read in the news or what they hear from the government.

The IEEE Standards Association has released a report calling for engineers to consider the impact their work will have on climate change, children, and society. The Institute of Electrical and Electronics Engineers (IEEE) is one of the largest organizations for computer scientists in the world. With hundreds of thousands of members, the group undertakes initiatives to create common standards and often consults organizations like the European Commission and OECD on matters of ethics and design principles. "It is imperative to move beyond business as usual and to prioritize the well-being of our children, starting with protecting their privacy and security online. If we fail to do this, their agency, mental health, and self-actualization as humans in any culture will be reliant on forces beyond their control," reads the report titled "Measuring What Matters in the Era of Global Warming and the Age of Algorithmic Promises." The whitepaper encapsulates change already underway at the IEEE that's in line with AI ethics principles released in spring 2019 after years of work, according to John Havens, director of the IEEE Global Initiative on Ethics of Autonomous & Intelligent Systems.

CMS launches healthcare outcomes challenge. Expect more artificial intelligence (AI) in healthcare in 2020. We will see AI used primarily in diagnostics and auditing. In each of these areas, AI promises to impose drastic changes on society. As these changes reverberate through organizations, old work patterns will be disrupted.

To kick off the Future Development blog in 2020, we present the fourth in a four-part series on the future of development. A couple of years ago, Vladimir Putin warned Russians that the country that led in technologies using artificial intelligence will dominate the globe. He was right to be worried. Russia is now a minor player, and the race seems now to be mainly between the United States and China. But don't count out the European Union just yet; the EU is still a fifth of the world economy, and it has underappreciated strengths.

It is hardly "breaking news" that technology is radically changing almost every aspect of our lives and the world around us - but we talk far less about the challenges that come along with this rapid evolution. And we certainly don't know the answer to the important question: what is the ultimate outcome of this Digital Revolution? To understand this issue, we should recognize exactly how fast things are really changing. A good metric for this purpose is the floating-point operation: a single mathematical calculation (like addition or multiplication) on two numbers that have decimal points. In 1954, IBM introduced the first mass-produced computer with dedicated floating-point arithmetic hardware. The IBM 704 was regarded as the only computer that could handle complex math at that time: it could execute up to 12,000 floating-point operations per second, or FLOPS.

A human can likely tell the difference between a turtle and a rifle. For quite some time, a subset of computer science research has been dedicated to better understanding how machine-learning models handle these "adversarial" attacks, which are inputs deliberately created to trick or fool machine-learning algorithms. While much of this work has focused on speech and images, recently, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) tested the boundaries of text. They came up with "TextFooler," a general framework that can successfully attack natural language processing (NLP) systems -- the types of systems that let us interact with our Siri and Alexa voice assistants -- and "fool" them into making the wrong predictions. One could imagine using TextFooler for many applications related to internet safety, such as email spam filtering, hate speech flagging, or "sensitive" political speech text detection -- which are all based on text classification models.

"In the world of imagination, it is possible to envisage a cognitively and emotionally intelligent chief executive, who happens also to be an inspiring public communicator... and the possessor of exceptional political skill and vision. In the real world, human imperfection is inevitable, but some imperfections are more disabling than others .... Beware the presidential contender who lacks emotional intelligence. Fred Greenstein, an emeritus professor of politics at Princeton, wrote this in his book "The Presidential Difference" (third edition, 2009), which surveys the characters of American presidents from Franklin D. Roosevelt to Barack Obama and seeks to glean the characteristics needed to be a good leader. In his book, Greenstein goes deeper into the popular American habit of ranking presidents. The genesis of this method is usually ascribed to the American historian Arthur Schlesinger. In the 1940s, Schlesinger discovered a relatively empty niche in his field, American history, ...