A fantastic game to play for a little escape from reality after a rough day, The Sims 4 has splendid gardening mechanics in the base game. If you've ever fantasized about owning a nice house with the perfect backyard for gardening, try The Sims 4. In her roundup of the best game additions available for purchase, product writer and reviewer Louryn Strampe recommends Seasons ($40). She writes, "If you're only getting one expansion, this is the one you want." In addition to dynamic weather, the Season pack unlocks a gardener career path, which lets you role-play as a botanist or floral designer. Devoted players searching for even more plant experiences may appreciate crafting homestead fantasies with the Cottage Living Expansion Pack ($40) or designing the ideal outdoor space for making out with the Romantic Garden Stuff ($10).
A neural network transforms input, the circles on the left, to output, on the right. How that happens is a transformation of weights, center, which we often confuse for patterns in the data itself. It's a commonplace of artificial intelligence to say that machine learning, which depends on vast amounts of data, functions by finding patterns in data. The phrase, "finding patterns in data," in fact, has been a staple phrase of things such as data mining and knowledge discovery for years now, and it has been assumed that machine learning, and its deep learning variant especially, are just continuing the tradition of finding such patterns. AI programs do, indeed, result in patterns, but, just as The fault, dear Brutus, lies not in our stars but in ourselves, the fact of those patterns is not something in the data, it is what the AI program makes of the data.
The deep learning field is progressing rapidly, and the latest work from Deepmind is a good example of this. Their Gato model is able to learn to play Atari games, generate realistic text, process images, control robotic arms, and more, all with the same neural network. Inspired by large-scale language models, Deepmind applied a similar approach but extended beyond the realm of text outputs. This new AGI (after Artificial General Intelligence) works as a multi-modal, multi-task, multi-embodiment network, which means that the same network (i.e. a single architecture with a single set of weights) can perform all tasks, despite involving inherently different kinds of inputs and outputs. While Deepmind's preprint presenting Gato is not very detailed, it is clear enough in that it is strongly rooted in transformers as used for natural language processing and text generation.
Memorial Day is a major shopping holiday in the US, but nobody wants to spend their long weekend scrolling through marketing emails. Let us save you the trouble. We scoured the web to find actual deals on the gear WIRED reviewers recommend. Below, you'll find great sales on everything from video games to furniture. Don't forget to check back, as we'll be updating this story throughout the weekend. Be sure to check out our other Memorial Day deals coverage, including the Best Memorial Day Mattress Deals, Best Memorial Day Outdoors Deals, Best Masturbation May Sex Tech Deals, and Best REI Anniversary Sale Deals for more. Updated on May 28: We've added a few new deals on Osprey packs, Instacart, Jins Sunglasses, Gravity Blanket, Overcooked 2, and Allform couches, and a link to the sale at Moment.
As artificial intelligence gets better at performing tasks once solely in the hands of humans, like driving cars, many see teaming intelligence as a next frontier. In this future, humans and AI are true partners in high-stakes jobs, such as performing complex surgery or defending from missiles. But before teaming intelligence can take off, researchers must overcome a problem that corrodes cooperation: humans often do not like or trust their AI partners. MIT Lincoln Laboratory researchers have found that training an AI model with mathematically "diverse" teammates improves its ability to collaborate with other AI it has never worked with before, in the card game Hanabi. Moreover, both Facebook and Google's DeepMind concurrently published independent work that also infused diversity into training to improve outcomes in human-AI collaborative games.
On July 3rd, Tom Cruise will be sixty years old. The fact that he does not look it, at all, even in IMAX closeups so tight you can study the grain of his tooth enamel, adds a note of cognitive dissonance to "Top Gun: Maverick," the long-aborning sequel in which he's called back to mentor a squad of younger stick-jockeys who address him as Pops and Old-Timer until he wins their respect in the air. Even for a physical performer like Cruise, sixty is no longer an expiration date. Mick Jagger blew by that milestone in 2003, as did Sylvester Stallone in 2006, and, thanks presumably to healthy habits and/or medical technology dreamt of only by science fiction, they're both still out there, doing a version of the kind of thing they've always done. But the level of performance expected of a Rolling Stone or an Expendable is one thing, and the work that Tom Cruise appears to demand of himself is something else entirely.
Using computing resources at the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory (Berkeley Lab), researchers at Argonne National Laboratory have succeeded in exploring important materials science questions and demonstrated progress using machine learning to solve difficult search problems. By adapting a machine-learning algorithm from board games such as AlphaGo, the researchers developed force fields for nanoclusters of 54 elements across the periodic table, a dramatic leap toward understanding their unique properties and proof of concept for their search method. The team published its results in Nature Communications in January. Depending on their scale--bulk systems of 100 nanometers versus nanoclusters of less than 100 nanometers--materials can display dramatically different properties, including optical and magnetic properties, discrete energy levels, and enhanced photoluminescence. These properties may lend themselves to new scientific and industry applications, and scientists can learn about them by developing force fields--computational models that estimate the potential energies between atoms in a molecule and between molecules--for each element or compound.
Technical advancements make skill-based matchmaking techniques better every year, enticing average audiences to play more. But those same changes have also left a sour taste in some players' mouths who publishers have a vested interest in keeping happy -- their live streams help market games. Game companies have the seemingly impossible task of satisfying both sides; on one end, the massive player base of everyday gamers that define their bottom line and, on the other, the pros and content creators they use as for PR for those same audiences. But if these systems are indeed built to maximize players' enjoyment, it can sometimes seem like they're not working very well. Hate for skill-based matchmaking is hardly a phenomenon confined to top streamers or salty Call of Duty players. As awareness about these algorithms grows, communities in "Valorant," "Overwatch," "Apex Legends" and even more casual games like "FIFA" and "Dead by Daylight" have all, at one point or another, sharply criticized matchmaking for reducing their enjoyment of the game.
Then Kasparov lurched out of his chair to walk toward the audience. At its finest moment, he later said, the machine "played like a god." For anyone interested in artificial intelligence, the grand master's defeat rang like a bell. Newsweek called the match "The Brain's Last Stand"; another headline dubbed Kasparov "the defender of humanity." If AI could beat the world's sharpest chess mind, it seemed that computers would soon trounce humans at everything--with IBM leading the way.