Humans learn from visual inputs at multiple timescales, both rapidly and flexibly acquiring visual knowledge over short periods, and robustly accumulating online learning progress over longer periods. Modeling these powerful learning capabilities is an important problem for computational visual cognitive science, and models that could replicate them would be of substantial utility in real-world computer vision settings. In this work, we establish benchmarks for both real-time and life-long continual visual learning. Our real-time learning benchmark measures a model's ability to match the rapid visual behavior changes of real humans over the course of minutes and hours, given a stream of visual inputs. Our life-long learning benchmark evaluates the performance of models in a purely online learning curriculum obtained directly from child visual experience over the course of years of development.

A precise understanding of why units in an artificial network respond to certain stimuli would constitute a big step towards explainable artificial intelligence. One widely used approach towards this goal is to visualize unit responses via activation maximization. These feature visualizations are purported to provide humans with precise information about the image features that cause a unit to be activated - an advantage over other alternatives like strongly activating dataset samples. If humans indeed gain causal insight from visualizations, this should enable them to predict the effect of an intervention, such as how occluding a certain patch of the image (say, a dog's head) changes a unit's activation. Here, we test this hypothesis by asking humans to decide which of two square occlusions causes a larger change to a unit's activation.Both a large-scale crowdsourced experiment and measurements with experts show that on average the extremely activating feature visualizations by Olah et al. (2017) indeed help humans on this task ($68 \pm 4$% accuracy; baseline performance without any visualizations is $60 \pm 3$%). However, they do not provide any substantial advantage over other visualizations (such as e.g.

Much of the US economy rests on AI's future. On this episode of podcast, cohost Joe Weisenthal breaks down why AI's impact on finance goes beyond billion-dollar investments. If you read any of WIRED's recent AI edition, you know that lots of people are spending lots of time talking about how the technology is revolutionizing pretty much everything--from coding to writing to accounting. You've also probably heard by now, from us or somebody else, that we might very well be in an economic bubble of AI origin, one wherein the billions and billions of dollars being funneled into the industry is creating an untenable economic scenario that could turn catastrophic. Of course, you may also have read that I'm really sick of being asked about AI . I'm still not sick, though, of asking other people about it--especially when they're much smarter about this stuff than I am. Enter Joe Weisenthal, the cohost of Bloomberg's fantastic podcast, and a former coworker of mine. Trust me: As someone who spent a year listening to Joe lose his mind in the office--loudly!--anytime the economy hiccuped, few people think more about our country's, and our planet's, financial circumstances than Joe does. And right now, Joe's concerns aren't strictly about what happens if or when that AI bubble bursts. His worries are more focused on what's going right and wrong with the US economy writ large. For this week's episode of, Joe and I talked about weird market indicators, US competition with China, and whether or not we should all prepare for an AI economic apocalypse. Nice to see you again. We were just talking about how [you] and I worked together--what was that, like nine years ago? I think you were there 2014, 2015, so maybe 10 years ago or something? Yeah, I worked at Bloomberg. I lasted about a year. But Joe, you were there, you were loud, you were proud, you were always very excited about the economy.

Minor comments - P1L24 M_ij 1 - P1L19: classically, - P2L54: determinEs - P3L111: what is G _2? Is it different from the Frobenius norm? - P3L138: the second line of the equation is wrong, since the matrix M does not appear in the rhs - P4L145: with high probability (without a) - P5L198: we propose an adaptive (check your spelling) - P5L200: I do not understand what do you mean by guarantee the performance.

Slack will soon begin rolling out what it calls "Slack AI" to its customers, featuring smart search, channel recaps, and summaries -- and a number of caveats, too. Slack has been trying to integrate AI into its conversational interface for about a year now, and some of this sounds pretty familiar. Slack was talking about a "Slack GPT" app last spring, with the eventual rollout of a sales-based AI, Einstein GPT, to leverage parent Salesforce's CRM technology. That's part of the message that Slack is reiterating today. Some of what Slack is talking about makes sense.

For a prolific art collector, Nicolai Tangen is remarkably relaxed about the prospect of masterpieces created by robots. The threat of AI-made paintings, impossible to distinguish from human brushstrokes, has sparked soul-searching and paranoia in the art world, but not with Tangen. "Hey, if it creates better art that's fantastic," says the Norwegian philanthropist, art historian and boss of the world's biggest sovereign wealth fund. "If you create something which is even more aesthetically pleasing, what's wrong about that?" Tangen's own gallery, a converted grain silo in the Norwegian seaside resort of Kristiansand, will open later this year to display one of the world's biggest collections of Nordic modernist art. Tangen has amassed more than 5,000 works by 300 artists.

Have you ever heard of Midjourney? Well, in case you did not know, it's a standalone research laboratory responsible for developing an AI program of the same name. This program generates pictures based on textual descriptions, similar to OpenAI's DALL-E and Stable Diffusion. You probably don't know a lot about this, but today my aim is to change that. According to the company's founder (David Holz) the company was already profitable in August 2022.

Artificial intelligence has already beaten chess. Hell, the most sophisticated AI systems have a very good chance against top players in the incredibly complicated game of Go. But, in the uber-complicated car-based soccer game of Rocket League, can an AI do a boosted 360 aerial bicycle kick power shot from the midline? Can it pinch a ball off the side ramp so precisely it sails into the goal at 90 MPH? No, at least not yet, but AI can apparently dribble like a madman. For more than a week, players have been driven up the wall (sometimes literally, in game) by machine learning-based AI that's been hacked into games of Rocket League.

While machine learning has advanced by leaps and bounds, it's hard to create an AI that's good at more than one thing. So, a machine could be trained with data to handle one class of programming challenges, but it would fail when given a different problem to tackle. So, the team decided to skip all the training on algorithms and code structure, instead treating it more like a translation problem. Programming challenges usually include a description of the task, and the resulting code submitted by a human participant is technically just an expression of the description. The AI works in two phases: It takes the description and converts it to an internal representation.

Grammarly is one of the major breakthroughs that AI has made in this decade. I am presuming that most of us know what Grammarly is and how we are hugely benefited by such a tool. What if I tell you that you can make your own minimal spell corrector tool easily without expertise in coding using Transformers. Well, let's not wait and dive into the post. So we will not be able to achieve performance similar to Grammarly but we can easily reach base accuracy by building a simple sequence to sequence model for spelling errors and zero-shot learner for the classification task.