Explains: Let our writers untangle the complex, messy world of technology to help you understand what's coming next.

In this paper, we revisit Johanna Drucker's question, "Is there a digital art history?" -- posed exactly a decade ago -- in the light of the emergence of large-scale, transformer-based vision models. While more traditional types of neural networks have long been part of digital art history, and digital humanities projects have recently begun to use transformer models, their epistemic implications and methodological affordances have not yet been systematically analyzed. We focus our analysis on two main aspects that, together, seem to suggest a coming paradigm shift towards a "digital" art history in Drucker's sense. On the one hand, the visual-cultural repertoire newly encoded in large-scale vision models has an outsized effect on digital art history. The inclusion of significant numbers of non-photographic images allows for the extraction and automation of different forms of visual logics. Large-scale vision models have "seen" large parts of the Western visual canon mediated by Net visual culture, and they continuously solidify and concretize this canon through their already widespread application in all aspects of digital life. On the other hand, based on two technical case studies of utilizing a contemporary large-scale visual model to investigate basic questions from the fields of art history and urbanism, we suggest that such systems require a new critical methodology that takes into account the epistemic entanglement of a model and its applications. This new methodology reads its corpora through a neural model's training data, and vice versa: the visual ideologies of research datasets and training datasets become entangled.

Despite concerns about whether students are using ChatGPT to cheat on exams or as a shortcut to doing their coursework, a new national survey shows students and teachers have quickly incorporated the new technology into their every day lives. Laila Ayala, a student at Comp Sci High in New York City, for instance, has used ChatGPT to research prompts for her debate team on the effect of AI on students, student mental health and whether the SAT and ACT should be abolished. In Kentucky, high school junior Zachary Clifton said he's used ChatGPT to create study guides for some of the college courses he takes at a nearby community college. Even as some school districts ban the artificial intelligence platform – which can quickly answers questions about nearly any subject it's asked – and some college professors find themselves becoming hypervigilant about whether students are using it to cheat, the new survey commissioned by the Walton Family Foundation and conducted by Impact Research found 22% of students use the chatbot to help them with coursework or in extracurricular activities "on a weekly basis or more." And more than half of teachers surveyed reported using ChatGPT at least once since its release, with 40% of teachers using it "at least once a week."

In the early two-thousands, Merlin Mann, a Web designer and avowed Macintosh enthusiast, was working as a freelance project manager for software companies. He had held similar roles for years, so he knew the ins and outs of the job; he was surprised, therefore, to find that he was overwhelmed--not by the intellectual aspects of his work but by the many small administrative tasks, such as scheduling conference calls, that bubbled up from a turbulent stream of e-mail messages. "I was in this batting cage, deluged with information," he told me recently. Why was I having such a hard time?" In the nineteen-nineties, the spread of e-mail had transformed knowledge work. With nearly all friction removed from professional communication, anyone could bother anyone else at any time. Many e-mails brought obligations: to answer a question, look into a lead, arrange a meeting, or provide feedback. Work lives that had once been sequential--two or three blocks of work, broken up by meetings and phone ...

The end of the 20th century was the moment of peak knowledge worker. The most valuable asset of a 21st-century institution, whether business or non-business, will be its knowledge workers and their productivity. But Drucker got it wrong. Two trends that were barely visible back then are reshaping the 21st century enterprise. Knowledge is still important, but today it has become a commodity. In the 21st century, value is shifting towards experiences and outcomes.

Just over fifty years ago, a French journalist, Jean-Jacques Servan-Schreiber, published his book, Le Défi Américain (aka The American Challenge, 1967). It presented the United States and Europe as engaged in a silent economic war. In that war, he wrote, Europe was being completely outclassed on all fronts in dealing with the Third Industrial Revolution (electronics, information technology, and automation). The invading industrial armies of the day--1960s giants such as General Motors and IBM--were becoming dominant in Europe because of stronger and more flexible management techniques, technological tools, and research capacity. The book became an international hit, selling an unprecedented 600,000 copies in France alone.

The late professor Seth Teller created 6.811 (Principles and Practices in Assistive Technologies, or PPAT) in the fall of 2011. Through his extensive experience developing assistive technologies (AT) at MIT, his compassion for making technology available to all, and his innovative approach and drive to build this class, student interest in PPAT and AT has grown steadily since. Following Teller's untimely death on July 1 this year, a group of former PPAT and AT students including his graduate student William Li SM '12, who TA'd the inaugural PPAT offering; Grace Teo PhD '14, a former student and member of the MIT Assistive Technology Club; and a core group of students who took the class in 2013 have formed a team to continue Teller's legacy through both the coninuation of PPAT and an outgrowth known as "AT Hack," a one-day workshop launched in spring 2014. Li and Teo, who will co-instruct this year's class, and three other members of the team will work with Professor Rob Miller, MIT MacVicar Faculty Fellow, member of the Computer Science and Artificial Intelligence Lab (CSAIL), and co-education officer of the Department of Electrocal Engineering and Computer Science (EECS). Every year since the inaugural offering of PPAT, Miller had worked with Teller to help develop and teach the course.

Which kinds of knowledge workers are at high risk of job loss thanks to smart machines? Usually we don't love getting that question, because the answer isn't the simple one interviewers are seeking. Many jobs include tasks that can and will be automated, but by the same token, almost all jobs have major elements that -- for the foreseeable future -- won't be possible for computers to handle. Our advice therefore can't boil down to a clear "avoid careers in a, b, and c" or "apply for jobs x, y, or z." And yet, we have to admit that there are some knowledge-work jobs that will simply succumb to the rise of the robots.

I, like a number of HR industry analysts, have been uncomfortable about some of the uses of algorithms, artificial intelligence, machine learning and other technologies that have been exploding on the HR scene in recent years. The single greatest concern is that individuals who have no training in statistics, correlation/causation distinctions, legal risk associated with machine generated recommendations, etc. might use these new technologies and expose their company to great risk and/or adversely impact the livelihoods of many innocent potential workers. In a nutshell, many of the new, cool, supercharged recruiting solutions can quickly and mechanically identify other potential job seekers with similar characteristics to current employee groups that have shown some measure of success or retention with the company. While on its surface that sounds admirable and cost-effective, the problem with these tools is that they rely on a test database which only includes existing employees. If a company has failed to hire many women or minorities in the past, then very few of them will appear in the solution's data population and thus will generate a statistically insignificant subset of individuals to establish a meaningful pattern.

A boosting algorithm converts a learning machine with error rate less than 50% to one with an arbitrarily low error rate. However, the algorithm discussed here depends on having a large supply of independent training samples. We show how to circumvent this problem and generate an ensemble of learning machines whose performance in optical character recognition problems is dramatically improved over that of a single network. We report the effect of boosting on four databases (all handwritten) consisting of 12,000 digits from segmented ZIP codes from the United State Postal Service (USPS) and the following from the National Institute of Standards and Testing (NIST): 220,000 digits, 45,000 upper case alphas, and 45,000 lower case alphas. We use two performance measures: the raw error rate (no rejects) and the reject rate required to achieve a 1% error rate on the patterns not rejected.