Three years ago, Google set an ambitious plan to address climate change by going "net zero", meaning it would release no more climate-changing gases into the air than it removes, by 2030. But a report from the company on Tuesday showed it is nowhere near meeting that goal. Rather than declining, its emissions grew 13 percent in 2023 over the year before. Compared with its baseline year of 2019, emissions have soared 48 percent. Google cited artificial intelligence and the demand it puts on data centres, which require massive amounts of electricity, for last year's growth.

Stochastic and adversarial data are two widely studied settings in online learning. But many optimization tasks are neither i.i.d. nor fully adversarial, which makes it of fundamental interest to get a better theoretical understanding of the world between these extremes. In this work we establish novel regret bounds for online convex optimization in a setting that interpolates between stochastic i.i.d. and fully adversarial losses. By exploiting smoothness of the expected losses, these bounds replace a dependence on the maximum gradient length by the variance of the gradients, which was previously known only for linear losses. In addition, they weaken the i.i.d. assumption by allowing, for example, adversarially poisoned rounds, which were previously considered in the related expert and bandit settings. In the fully i.i.d. case, our regret bounds match the rates one would expect from results in stochastic acceleration, and we also recover the optimal stochastically accelerated rates via online-to-batch conversion. In the fully adversarial case our bounds gracefully deteriorate to match the minimax regret. We further provide lower bounds showing that our regret upper bounds are tight for all intermediate regimes in terms of the stochastic variance and the adversarial variation of the loss gradients.

The instrumental variable (IV) approach is a widely used way to estimate the causal effects of a treatment on an outcome of interest from observational data with latent confounders. A standard IV is expected to be related to the treatment variable and independent of all other variables in the system. However, it is challenging to search for a standard IV from data directly due to the strict conditions. The conditional IV (CIV) method has been proposed to allow a variable to be an instrument conditioning on a set of variables, allowing a wider choice of possible IVs and enabling broader practical applications of the IV approach. Nevertheless, there is not a data-driven method to discover a CIV and its conditioning set directly from data. To fill this gap, in this paper, we propose to learn the representations of the information of a CIV and its conditioning set from data with latent confounders for average causal effect estimation. By taking advantage of deep generative models, we develop a novel data-driven approach for simultaneously learning the representation of a CIV from measured variables and generating the representation of its conditioning set given measured variables. Extensive experiments on synthetic and real-world datasets show that our method outperforms the existing IV methods.

In the neuroimaging experiment, 40 volunteers listened to a series of sad or happy musical excerpts, while their brains were scanned using MRI. This was conducted at USC's Brain and Creativity Institute by Assal Habibi, an assistant professor of psychology at USC Dornsife College of Letters, Arts and Sciences, and her team, including Matthew Sachs, a postdoctoral scholar currently at Columbia University. To measure physical reaction, 60 people listened to music on headphones, while their heart activity and skin conductance were measured. The same group also rated the intensity of emotion (happy or sad) from 1 to 10 while listening to the music. Then, the computer scientists crunched the data using AI algorithms to determine which auditory features people responded to consistently. In the past, neuroscientists trying to better understand the impact of music on the body, brain and emotions have analyzed MRI brain scans over very short segments of time--for instance, looking at the brain reacting to two seconds of music.

A "tech tax" is necessary if the world is to avoid a dystopian future in which AI leads to a concentration of global wealth in the hands of a few thousand people, influential economist Dr Jeffrey Sachs has warned. Speaking to the Guardian, Sachs backed calls for taxation aimed at the largest tech companies, arguing that new technologies were dramatically shifting the income distribution worldwide "from labour to intellectual property (IP) and other capital income." "So rather than cutting capital income taxation, as we've been doing in a race to the bottom, we ought to be finding ways to tax capital income and IP income," Sachs added. "Things like the proposed tech tax are actually a very good idea. The specific form of it is debatable, but the idea is that five companies are worth $3.5tn, basically because of network externalities and information monopolies, and therefore are absolutely right for efficient taxation."

As the volume of digital content grows exponentially, marketers are spending more time than ever trying to understand what resonates with their customers. Over the next couple of years, artificial intelligence (AI) could boost these efforts by pinpointing the best emotional appeal, subject matter, style, tone and sentiment to focus on. U.S. marketers spent more than $10 billion on content in 2016, according to a Forrester estimate. For many marketers, their content marketing strategy now includes a wide range of tactics like social media posts, short- and long-form video, live video and sponsored articles. However, for most, content is delivered with a piecemeal approach.

As the volume of digital content grows exponentially, marketers are spending more time than ever trying to understand what resonates with their customers. Over the next couple of years, artificial intelligence (AI) could boost these efforts by pinpointing the best emotional appeal, subject matter, style, tone and sentiment to focus on. U.S. marketers spent more than $10 billion on content in 2016, according to a Forrester estimate. For many marketers, their content marketing strategy now includes a wide range of tactics like social media posts, short- and long-form video, live video and sponsored articles. However, for most, content is delivered with a piecemeal approach.

As lower-skill jobs are swept away by technology, economists are wondering what kinds of skills workers will need -- and how the economic benefits of automation will be distributed. Perhaps the hottest topic in economics these days is how far robots and artificial intelligence (AI) will go in replacing humans in the workplace -- and how quickly the changes will unfold. For decades, technology has been replacing mid-level jobs in the United States and other advanced economies. But the pace has quickened, with the effects billowing through not just automotive factories and electronics facilities but also law offices, operating rooms, and the roads we drive on. Some economists, including MIT's Erik Brynjolfsson and Andrew McAfee, have argued that, in addition to eliminating jobs, technology will also open the way to new ones.

As lower-skill jobs are swept away by technology, economists are wondering what kinds of skills workers will need -- and how the economic benefits of automation will be distributed. Perhaps the hottest topic in economics these days is how far robots and artificial intelligence (AI) will go in replacing humans in the workplace -- and how quickly the changes will unfold. For decades, technology has been replacing mid-level jobs in the United States and other advanced economies. But the pace has quickened, with the effects billowing through not just automotive factories and electronics facilities but also law offices, operating rooms, and the roads we drive on. Some economists, including MIT's Erik Brynjolfsson and Andrew McAfee, have argued that, in addition to eliminating jobs, technology will also open the way to new ones.