Models in these systems are often developed and trained independently, which raises an obvious concern: Can improving a machine-learning model make the overall system worse?

Machine-learning systems such as self-driving cars or virtual assistants are composed of a large number of machine-learning models that recognize image content, transcribe speech, analyze natural language, infer preferences, rank options, etc. Models in these systems are often developed and trained independently, which raises an obvious concern: Can improving a machine-learning model make the overall system worse? We answer this question affirmatively by showing that improving a model can deteriorate the performance of downstream models, even after those downstream models are retrained. Such self-defeating improvements are the result of entanglement between the models in the system. We perform an error decomposition of systems with multiple machine-learning models, which sheds light on the types of errors that can lead to self-defeating improvements. We also present the results of experiments which show that self-defeating improvements emerge in a realistic stereo-based detection system for cars and pedestrians.

In each case, the students confessed to using such systems and agreed to rewrite the assignments themselves. With all the panic about how students might use these systems to get around the burden of actually learning, we often forget that as of 2023, the systems don't work well at all. It was easy to spot these fraudulent essays. They used text that did not respond to the prompt we had issued to students. Or they just sounded unlike what a human would write.

A dire threat to public health can emerge from a huge variety of sources--for example, infectious diseases, a spate of drug overdoses, or exposures to toxic chemicals. Federal, state, and local health departments must respond rapidly to disease outbreaks and other emerging bio-threats. While the current automated systems for "syndromic surveillance" can help by monitoring health data and detecting disease clusters, they are not able to detect clusters with rare or previously unseen symptomology. The method is incorporated in an automated system that can enable public health practitioners to respond more quickly and effectively in the future to fast-emerging threats, including those that are unusual or novel. "Existing systems are good at detecting outbreaks of diseases that we already know about and are actively looking for, like flu or COVID," comments NYU professor Daniel B. Neill, the senior author of the study and director of the ML4G Lab.

One of the most useful texts for anyone covering the tech industry is George Orwell's celebrated essay, Politics and the English Language. Orwell's focus in the essay was on political use of the language to, as he put it, "make lies sound truthful and murder respectable and to give an appearance of solidity to pure wind". But the analysis can also be applied to the ways in which contemporary corporations bend the language to distract attention from the sordid realities of what they are up to. The tech industry has been particularly adept at this kind of linguistic engineering. "Sharing", for example, is clicking on a link to leave a data trail that can be used to refine the profile the company maintains about you.

In the last few decades, machine learning has become increasingly popular as a means to support decision-making. From banks and insurance companies to internet service providers, dentists and even the supermarkets where we do our weekly shopping, this technology is ever more pervasive in our lives. Its ubiquitous presence, however, is not just limited to the private realm. Public institutions have also begun using this technology to improve a multitude of processes, for example, to prevent crime, detect tax fraud and award subsidies and grants, among others. To a large extent, machine learning's success can be explained by its promise of greater coherence and the resulting perception of greater objectivity. In this sense, claims abound about how using machine learning can help us "make better decisions".

Nice to hear from you, I hope you are well and life is not to hectic. "For myself, it is the front door into ML that is more worrying." What actually worries me is not "the method" of perversion of which ML appears to have endless varieties at every point (thus is not fit for honest purpose). As I've pointed out before, in "The King Game" there is the notion of "The Godhead". Where the King is a direct conduit to God's words thus wishes.

Long before the technology even existed in the real world, the concept of artificial intelligence has long been a topic of fixation for writers. From cautionary tales and science fiction epics to nonfictional explorations of the implications of AI in our modern world, artificial intelligence seems to be an endlessly fascinating subject of books both big and small. As such, there are all kinds of truly exceptional books about artificial intelligence out there for you to read, enjoy, and maybe even learn a thing or two from. As to be expected, these books about artificial intelligence truly run the gamut. Beyond simply falling under both fiction and nonfiction, artificial intelligence books cover topics ranging from the future to the past, from work to society, from computing to critiques… and all sorts of other topics along the way.

Scientists and engineers are constantly developing new materials with unique properties that can be used for 3D printing, but figuring out howto print with these materials can be a complex, costly conundrum. Often, an expert operator must use manual trial-and-error -- possibly making thousands of prints -- to determine ideal parameters that consistently print a new material effectively. These parameters include printing speed and how much material the printer deposits. MIT researchers have now used artificial intelligence to streamline this procedure. They developed a machine-learning system that uses computer vision to watch the manufacturing process and then correct errors in how it handles the material in real-time.