AI

I. We had crash-landed on the planet. We were far from home. The spaceship could not be repaired, and the rescue beacon had failed. Besides me, only the astrogator, part of the captain, and the ship’s AI mind were left.  Outside, the atmosphere registered as hostile to most organisms. We huddled in the lifeboat, which was inoperable but still held air. Vast storms buffeted our cockleshell shelter, although we knew from prior readings that other areas remained calm. All that remained to us was to explore, if we wanted to live. The captain gave me the sole weapon. She tasked the astrogator with carrying some tools that would not unduly weigh him down. Little existed on the planet except deserts of snow. But alien artifacts lay in an area near us. We were an exploration team, so this discovery had oddly comforted us, even though we had been on our way elsewhere. The massive systems failure had no discernible source, and the planet had been our only choice for landfall. The artifacts took the form of 13 domes, spread out over that hostile terrain. The domes had been linked by cables just below shoulder level, threaded through the tops of metal posts at irregular intervals. Whether intended or not, these cables and rods formed a series of paths between the domes.  Before our instruments failed, the AI had reported that the domes appeared to have a heat signature. The cables pulsed under our grip in a way that teased promised warmth far ahead. It took some time to get used to the feeling. The shortest path between domes was a thousand miles long. The longest path was 10 thousand miles long. Our suit technology was good: A suit could recycle water, generate food, create oxygen. It could push us into various states of near hibernation while motors in the legs drove us forward. For the captain, the suit would compensate for having lost her legs and ease her pain. We estimated we could reach the nearest path and follow it to the nearest dome … and that was it. If the dome had life support capabilities, or even just a way to replenish our suits, we would live. Otherwise, we would probably die. We revised the estimate of our survival downward when we reached the path and soon encountered the skeletons of dead astronauts littering the way. In all shapes and sizes, cocooned within their suits. Their huddled forms under the snow displayed a serenity at odds with their fate. But when I wiped the frost from face plates, we saw the extremity of their suffering. It is difficult to explain how we felt walking among so many fatalities. So many dead first contacts.  We no longer had to puzzle over the systems failure. Spaceships came here to crash, and intelligent entities came here to die, for whatever reason. We could not presume our fate would be any different, and adjusted our expectations accordingly. The AI’s platitudes about courage did not raise morale. There were too many lost there in the frozen wastes.  Here were the ghastly emissaries of hundreds of spacefaring species we had never before encountered. The number of the bodies and their haphazard positioning hampered our ability to make progress to the dome. The AI estimated our chances of survival at below 50% for the first time. We would starve in our suits as the motors propelled us forward. We would become desiccated and exist in an elongation of our thoughts that made us weak and stupid until the light winked out. But still, we had no choice. So even in places where the dead in their suits were piled high, we would simply plunge forward, over and through them, headed for the dome.  What we would find there, as I have said, we did not know. But we were in an area of the galaxy where ancient civilizations had died out millions of years ago. We had been on our way to a major site, an ancient city on a moon with no atmosphere in a wilderness of stars.  Although our emotions fluctuated, a professional awe and curiosity about the dead eventually came over us. This created much debate over the comms. We had made a discovery for the ages, but our satisfaction was bittersweet. Even if we lived longer than expected, we would never return home, never see our friends or family again. The AI might continue on after we were dead, but I doubt it envied being the one to report on our discovery centuries hence. And to who? Here were the ghastly emissaries of hundreds of spacefaring species we had never before encountered. Their suits displayed an extraordinary range, although our examination was cursory. Some even appeared to be made out of scales and other biological substances from their home worlds, giving us further clues as to their origins.  The burial of the suits by snow and the lack of access to anything other than a screaming face or faces, often distorted by time and ice, worked against recording much usable data. This issue was compounded in those cases where the suit was part of the organism and they had not needed any “artificial skin,” as the AI put it, to survive harsh conditions. That many had died despite appearing well-­prepared for the planet’s environment sobered us up even before our own suits dispensed drugs to help our mental states.  After a time, each face seemed to express some aspect of our own stress and terror at the seriousness of our situation. After a time, the sheer welter of detail defeated us and caused us extreme distress. The captain made the observation that even one instance of alien contact might cause physiological and mental conditions, including anxiety, stress, fatigue. Here, we were constantly encountering the alien dead of what seemed at times an infinite number of civilizations.  We stopped recording. We recommitted ourselves to the slog toward the nearest dome.  The captain’s

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Constellations  —Constellations is a short story by Jeff VanderMeer, the author of the critically acclaimed, bestselling Southern Reach series.   A spacecraft has crash-landed on a hostile planet. The only survivors are three members of the exploration team and the ship’s AI mind.   Little exists on the planet except deserts of snow. But alien artifacts lie nearby, in the form of 13 domes, spread across the terrain. Linked by cables threaded through metal posts, the domes form a series of paths—the only hope for life support.  As the team treks across the frozen hellscape, they discover the remains of countless astronauts from unknown species who followed the same route before them. Is their trail a path to salvation, or a cosmic trap? Read the rest of this short story in full.  This story is from the next issue of our print magazine, packed with stories all about nature. Subscribe now to read the full thing when it lands on Wednesday, April 22.  The must-reads  I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.  1 OpenAI has joined Anthropic in curbing an AI release over security fears Only select partners will get its new cybersecurity tool. (Axios)  + Anthropic said only yesterday that its new AI is too dangerous for the public. (NBC News) + Top models may not be so public going forward. (Bloomberg $)  + The US has summoned bank CEOs to discuss the risks. (FT $)   2 Florida is investigating OpenAI over an alleged role in a shooting  ChatGPT may have helped someone plan a mass shooting in Florida. (WSJ $)  + OpenAI has backed a bill that would limit AI liability for deaths. (Wired $)  + The family of a victim plans to sue the company. (Guardian)  + AI’s role in delusions is dividing opinion. (MIT Technology Review)   3 Volkswagen is ditching EV production for more gasoline models  The carmaker will stop making its top electric vehicle in the US. (NYT $)  + Instead, it will concentrate on developing a new SUV. (Ars Technica)  + Western carmakers are retreating from electric vehicles. (Guardian)  4 Elon Musk’s xAI has sued Colorado over an AI anti-discrimination law  It’s the first state bill of its kind. (Bloomberg $)  + xAI says it will force the firm to “promote the state’s ideological views.” (FT $)  5 A fifth of US employees say AI now does parts of their job  The survey found half of US adults used AI in the past week. (NBC News)  + Missing data could shed light on AI’s job impact. (MIT Technology Review)   6 Google DeepMind’s CEO wants to automate drug design  He hopes to develop AI capable of curing all diseases. (The Economist)  + A scientist is using AI to hunt for antibiotics. (MIT Technology Review)  7 China’s Unitree is launching a viral robot on the international marketR1, its cheapest humanoid, will go on sale outside China next week. (SCMP)+ Gig workers are training humanoids at home. (MIT Technology Review) 8 An experiment on Artemis II astronauts could reshape space medicineChips containing their cells will model spaceflight’s effects. (WP $) 9 A pro-Iran meme machine is trolling Trump with AI Lego cartoonsThe videos have racked up millions of views. (Wired $) + You can learn to love AI slop. (MIT Technology Review) 10 Short breaks could erase 10 years of social media brain damage Studies show that a two-week detox could have a dramatic benefit. (WP $)  Quote of the day  “AI should advance mankind, not destroy it. We’re demanding answers on OpenAI’s activities that have hurt kids, endangered Americans, and facilitated the recent FSU mass shooting.” —Florida Attorney General James Uthmeier explains on X why he’s probing OpenAI.  One More Thing  TOM HUMBERSTONE It’s time to retire the term “user”  People have been called “users” for a long time. Often, it’s the right word to describe people who use software. But “users” is also unspecific enough to refer to just about everyone. It can accommodate almost any big idea or long-term vision.  We use—and are used by—computers and platforms and companies. The label “user” suggests these interactions are deeply transactional, but they’re frequently quite personal. Is it time for a more human vocabulary? Read the full story.  —Taylor Majewski  We can still have nice things  A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.)  + This flawless levitation trick will leave you questioning the laws of physics.+ The World Press Photo winners expose the beauty (and brutality) of our planet.+ Over 3 million pink flamingos gathered to create a stunning pink horizon.+ Behold the galaxy’s enormity in this comparison of its largest known star to Earth

A deep neural network can be understood as a geometric system, where each layer reshapes the input space to form increasingly complex decision boundaries. For this to work effectively, layers must preserve meaningful spatial information — particularly how far a data point lies from these boundaries — since this distance enables deeper layers to build rich, non-linear representations. Sigmoid disrupts this process by compressing all inputs into a narrow range between 0 and 1. As values move away from decision boundaries, they become indistinguishable, causing a loss of geometric context across layers. This leads to weaker representations and limits the effectiveness of depth. ReLU, on the other hand, preserves magnitude for positive inputs, allowing distance information to flow through the network. This enables deeper models to remain expressive without requiring excessive width or compute. In this article, we focus on this forward-pass behavior — analyzing how Sigmoid and ReLU differ in signal propagation and representation geometry using a two-moons experiment, and what that means for inference efficiency and scalability. Setting up the dependencies Copy CodeCopiedUse a different Browser import numpy as np import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec from matplotlib.colors import ListedColormap from sklearn.datasets import make_moons from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split Copy CodeCopiedUse a different Browser plt.rcParams.update({ “font.family”: “monospace”, “axes.spines.top”: False, “axes.spines.right”: False, “figure.facecolor”: “white”, “axes.facecolor”: “#f7f7f7”, “axes.grid”: True, “grid.color”: “#e0e0e0”, “grid.linewidth”: 0.6, }) T = { “bg”: “white”, “panel”: “#f7f7f7”, “sig”: “#e05c5c”, “relu”: “#3a7bd5”, “c0”: “#f4a261”, “c1”: “#2a9d8f”, “text”: “#1a1a1a”, “muted”: “#666666”, } Creating the dataset To study the effect of activation functions in a controlled setting, we first generate a synthetic dataset using scikit-learn’s make_moons. This creates a non-linear, two-class problem where simple linear boundaries fail, making it ideal for testing how well neural networks learn complex decision surfaces. We add a small amount of noise to make the task more realistic, then standardize the features using StandardScaler so both dimensions are on the same scale — ensuring stable training. The dataset is then split into training and test sets to evaluate generalization. Finally, we visualize the data distribution. This plot serves as the baseline geometry that both Sigmoid and ReLU networks will attempt to model, allowing us to later compare how each activation function transforms this space across layers. Copy CodeCopiedUse a different Browser X, y = make_moons(n_samples=400, noise=0.18, random_state=42) X = StandardScaler().fit_transform(X) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.25, random_state=42 ) fig, ax = plt.subplots(figsize=(7, 5)) fig.patch.set_facecolor(T[“bg”]) ax.set_facecolor(T[“panel”]) ax.scatter(X[y == 0, 0], X[y == 0, 1], c=T[“c0″], s=40, edgecolors=”white”, linewidths=0.5, label=”Class 0″, alpha=0.9) ax.scatter(X[y == 1, 0], X[y == 1, 1], c=T[“c1″], s=40, edgecolors=”white”, linewidths=0.5, label=”Class 1″, alpha=0.9) ax.set_title(“make_moons — our dataset”, color=T[“text”], fontsize=13) ax.set_xlabel(“x₁”, color=T[“muted”]); ax.set_ylabel(“x₂”, color=T[“muted”]) ax.tick_params(colors=T[“muted”]); ax.legend(fontsize=10) plt.tight_layout() plt.savefig(“moons_dataset.png”, dpi=140, bbox_inches=”tight”) plt.show() Creating the Network Next, we implement a small, controlled neural network to isolate the effect of activation functions. The goal here is not to build a highly optimized model, but to create a clean experimental setup where Sigmoid and ReLU can be compared under identical conditions. We define both activation functions (Sigmoid and ReLU) along with their derivatives, and use binary cross-entropy as the loss since this is a binary classification task. The TwoLayerNet class represents a simple 3-layer feedforward network (2 hidden layers + output), where the only configurable component is the activation function. A key detail is the initialization strategy: we use He initialization for ReLU and Xavier initialization for Sigmoid, ensuring that each network starts in a fair and stable regime based on its activation dynamics. The forward pass computes activations layer by layer, while the backward pass performs standard gradient descent updates. Importantly, we also include diagnostic methods like get_hidden and get_z_trace, which allow us to inspect how signals evolve across layers — this is crucial for analyzing how much geometric information is preserved or lost. By keeping architecture, data, and training setup constant, this implementation ensures that any difference in performance or internal representations can be directly attributed to the activation function itself — setting the stage for a clear comparison of their impact on signal propagation and expressiveness. Copy CodeCopiedUse a different Browser def sigmoid(z): return 1 / (1 + np.exp(-np.clip(z, -500, 500))) def sigmoid_d(a): return a * (1 – a) def relu(z): return np.maximum(0, z) def relu_d(z): return (z > 0).astype(float) def bce(y, yhat): return -np.mean(y * np.log(yhat + 1e-9) + (1 – y) * np.log(1 – yhat + 1e-9)) class TwoLayerNet: def __init__(self, activation=”relu”, seed=0): np.random.seed(seed) self.act_name = activation self.act = relu if activation == “relu” else sigmoid self.dact = relu_d if activation == “relu” else sigmoid_d # He init for ReLU, Xavier for Sigmoid scale = lambda fan_in: np.sqrt(2 / fan_in) if activation == “relu” else np.sqrt(1 / fan_in) self.W1 = np.random.randn(2, 8) * scale(2) self.b1 = np.zeros((1, 8)) self.W2 = np.random.randn(8, 8) * scale(8) self.b2 = np.zeros((1, 8)) self.W3 = np.random.randn(8, 1) * scale(8) self.b3 = np.zeros((1, 1)) self.loss_history = [] def forward(self, X, store=False): z1 = X @ self.W1 + self.b1; a1 = self.act(z1) z2 = a1 @ self.W2 + self.b2; a2 = self.act(z2) z3 = a2 @ self.W3 + self.b3; out = sigmoid(z3) if store: self._cache = (X, z1, a1, z2, a2, z3, out) return out def backward(self, lr=0.05): X, z1, a1, z2, a2, z3, out = self._cache n = X.shape[0] dout = (out – self.y_cache) / n dW3 = a2.T @ dout; db3 = dout.sum(axis=0, keepdims=True) da2 = dout @ self.W3.T dz2 = da2 * (self.dact(z2) if self.act_name == “relu” else self.dact(a2)) dW2 = a1.T @ dz2; db2 = dz2.sum(axis=0, keepdims=True) da1 = dz2 @ self.W2.T dz1 = da1 * (self.dact(z1) if self.act_name == “relu” else self.dact(a1)) dW1 = X.T @ dz1; db1 = dz1.sum(axis=0, keepdims=True) for p, g in [(self.W3,dW3),(self.b3,db3),(self.W2,dW2), (self.b2,db2),(self.W1,dW1),(self.b1,db1)]: p -= lr * g def train_step(self, X, y, lr=0.05): self.y_cache = y.reshape(-1, 1) out = self.forward(X, store=True) loss = bce(self.y_cache, out) self.backward(lr) return loss def get_hidden(self, X, layer=1): “””Return post-activation values for layer 1 or 2.””” z1 = X @ self.W1 + self.b1; a1 = self.act(z1) if

A rare warm spell in January melted enough snow to uncover Cornell University’s newest athletic field, built for field hockey. Months before, it was a meadow teeming with birds and bugs; now it’s more than an acre of synthetic turf roughly the color of the felt on a pool table, almost digital in its saturation. The day I walked up the hill from a nearby creek to take a look, the metal fence around the field was locked, but someone had left a hallway-size piece of the new simulated grass outside the perimeter. It was bristly and tough, but springy and squeaky under my booted feet. I could imagine running around on it, but it would definitely take some getting used to. My companion on this walk seemed even less favorably disposed to the thought. Yayoi Koizumi, a local environmental advocate, has been fighting synthetic-turf projects at Cornell since 2023. A petite woman dressed that day in a faded plum coat over a teal vest, with a scarf the colors of salmon, slate, and sunflowers, Koizumi compulsively picked up plastic trash as we walked: a red Solo cup, a polyethylene Dunkin’ container, a five-foot vinyl panel. She couldn’t bear to leave this stuff behind to fragment into microplastic bits—as she believes the new field will. “They’ve covered the living ground in plastic,” she said. “It’s really maddening.”  The new pitch is one part of a $70 million plan to build more recreational space at the university. As of this spring, Cornell plans to install something like a quarter million square feet of synthetic grass—what people have colloquially called “astroturf” since the middle of the last century. University PR says it will be an important part of a “health-promoting campus” that is “supportive of holistic individual, social, and ecological well-being.” Koizumi runs an anti-plastic environmental group called Zero Waste Ithaca, which says that’s mostly nonsense. This fight is more than just the usual town-versus-gown tension. Synthetic turf used to be the stuff of professional sports arenas and maybe a suburban yard or two; today communities across the United States are debating whether to lay it down on playgrounds, parks, and dog runs. Proponents say it’s cheaper and hardier than grass, requiring less water, fertilizer, and maintenance—and that it offers a uniform surface for more hours and more days of the year than grass fields, a competitive advantage for athletes and schools hoping for a more robust athletic program. But while new generations of synthetic turf look and feel better than that mid-century stuff, it’s still just plastic. Some evidence suggests it sheds bits that endanger users and the environment, and that it contains PFAS “forever chemicals”—per- and polyfluoroalkyl substances, which are linked to a host of health issues. The padding within the plastic grass is usually made from shredded tires, which might also pose health risks. And plastic fields need to be replaced about once a decade, creating lots of waste. Yet people are buying a lot of the stuff. In 2001, Americans installed just over 7 million square meters of synthetic turf, just shy of 11,000 metric tons. By 2024, that number was 79 million square meters—enough to carpet all of Manhattan and then some, almost 120,000 metric tons. Synthetic turf covers 20,000 athletic fields and tens of thousands of parks, playgrounds, and backyards. And the US is just 20% of the global market.  Where real estate is limited and demand for athletic facilities is high, artificial turf is tempting. “It all comes down to land and demand.” Frank Rossi, professor of turf science, Cornell Those increases worry folks who study microplastics and environmental pollution. Any actual risk is hard to parse; the plastic-making industry insists that synthetic fields are safe if properly installed, but lots of researchers think that isn’t so. “They’re very expensive, they contain toxic chemicals, and they put kids at unnecessary risk,” says Philip Landrigan, a Boston College epidemiologist who has studied environmental toxins like lead and microplastics. But at Cornell, where real estate is limited and demand for athletic facilities is high, synthetic turf was a tempting option. As Frank Rossi, a professor of turf science at Cornell, told me: “It all comes down to land and demand.” In 1965, Houston’s new, domed base­ball stadium was an icon of space-age design. But the Astrodome had a problem: the sun. Deep in the heart of Texas, it shined brightly through the Astrodome’s skylights—so much so that players kept missing fly balls. So the club painted over the skylights. Denied sunlight, the grass in the outfield withered and died. A replacement was already in the works. In the late 1950s a Ford Foundation–funded educational laboratory determined that a soft, grasslike surface material would give city kids more places to play outside and had prevailed upon the Monsanto corporation to invent one. The result was clipped blades of nylon stuck to a rubber base, which the company called ChemGrass. Down it went into Houston’s outfield, where it got a new, buzzier name: AstroTurf. Workers lay artificial turf at the Astrodome in Houston on July 13, 1966. Developed by Monsanto, the material was originally known as ChemGrass but was later renamed AstroTurf after the stadium.AP PHOTO/ED KOLENOVSKY, FILE That first generation of simulated lawn was brittle and hard, but quality has improved. Today, there are a few competing products, but they’re all made by extruding a petroleum-based polymer—that’s plastic—through tiny holes and then stitching or fusing the resulting fibers to a carpetlike bottom. That gets attached to some kind of padding, also plastic. In the 1970s the industry started layering that over infill, usually sand; by the 1990s, “third generation” synthetic turf had switched to softer fibers made of polyethylene. Beneath that, they added infill that combined sand and a soft, cheap shredded rubber made from discarded automobile tires, which pile up by the hundreds of millions every year. This “crumb rubber” provides padding and fills spaces between the blades and the backing. In the early 1980s, nearly

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Is fake grass a bad idea? The AstroTurf wars are far from over.  In 2001, Americans installed just over 7 million square meters of synthetic turf. By 2024, that number was 79 million square meters—enough to carpet all of Manhattan and then some. The increase worries folks who study microplastics and environmental pollution.   While the plastic-making industry insists that synthetic fields are safe if properly installed, lots of researchers think that isn’t so. Find out why AstroTurf has ignited heated debates. —Douglas Main  This story is from the next issue of our print magazine, packed with stories all about nature. Subscribe now to read the full thing when it lands on Wednesday, April 22.  Mustafa Suleyman: AI development won’t hit a development wall anytime soon—here’s why  —Mustafa Suleyman, Microsoft AI CEO and Google DeepMind co-founder  The skeptics keep predicting that AI compute will soon hit a wall—and keep getting proven wrong. To understand why that is, you need to look at the forces driving the AI explosion.   Three advances are enabling exponential progress: faster basic calculators, high-bandwidth memory, and technologies that turn disparate GPUs into enormous supercomputers. Where does all this get us? Read the full op-ed on the future of AI development to learn more.   Desalination technology, by the numbers  —Casey Crownhart  When I started digging into desalination technology for a new story, I couldn’t help but obsess over the numbers.  I knew on some level that desalination—pulling salt out of seawater to produce fresh water—was an increasingly important technology, especially in water-stressed regions including the Middle East. But just how much some countries rely on desalination, and how big a business it is, still surprised me. Here are the extraordinary numbers behind the crucial water source.  This story is from The Spark, our weekly newsletter on the tech that could combat the climate crisis. Sign up to receive it in your inbox every Wednesday.  The must-reads  I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.  1 Meta has launched the first AI model from its Superintelligence LabsMuse Spark is the company’s first model in a year. (Reuters $) + The closed model brings reasoning capabilities to the Meta AI app. (Engadget) + It’s built by Meta’s Superintelligence Labs, the unit led by Alexandr Wang. (TechCrunch)  2 Anthropic has lost a bid to pause the Pentagon’s blacklisting An appeals court in Washington, DC denied the request. (CNBC) + A California judge had temporarily blocked the blacklisting in March. (NPR) + The mixed rulings leave Anthropic in a legal limbo. (Wired $) + And open doors for smaller AI rivals. (Reuters $)  3 New evidence suggests Adam Back invented Bitcoin The British cryptographer may be the real Satoshi Nakamoto. (NYT $) + Back denies the claims. (BBC) + There’s a dark side to crypto’s permissionless dream. (MIT Technology Review)  4 Gen Z is cooling on AI The share feeling angry about it has risen from 22% to 31% in a year. (Axios) + Anti-AI protests are also growing. (MIT Technology Review)  5 War in the Gulf could tilt the cloud race toward China Huawei is pitching “multi-cloud” resilience to Gulf clients. (Rest of World)  6 Meta has killed a leaderboard of its AI token users It showed the top 250 users. (The Information $) + Meta blamed data leaks for the shutdown. (Fortune) + It encouraged “tokenmaxxing,” a growing phenomenon in Big Tech. (NYT $)  7 Did Artemis II really tell us anything new about space? Or was it primarily a PR exercise? (Ars Technica)  8 Israeli attacks have brutally exposed Lebanon’s digital infrastructure It’s managing a modern crisis without modern technology. (Wired $)  9 AI models could offer mathematicians a common language They hope it will simplify the process of verifying proofs. (Economist)   10 A “self-doxing’ rave is helping trans people stay safe online It’s among a series of digital self-defenses. (404 Media)  Quote of the day  “I feel like anything that I’m interested in has the potential of maybe getting replaced, even in the next few years.”  —Sydney Gill, a freshman at Rice University, tells the New York Times why she’s soured on AI.  One More Thing  A view inside ATLAS,one of two general-purpose detectors at the Large Hadron Collider.MAXIMILIEN BRICE/CERN Inside the hunt for new physics at the world’s largest particle collider  In 2012, data from CERN’s Large Hadron Collider (LHC) unearthed a particle called the Higgs boson. The discovery answered a nagging question: where do fundamental particles, such as the ones that make up all the protons and neutrons in our bodies, get their mass? But now particle physicists have reached an impasse in their quest to discover, produce, and study new particles at colliders. Find out what they’re trying to do about it. —Dan Garisto  We can still have nice things  A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.)  + Enjoy this tale of the “joke” sound that accidentally defined 90s rave culture. + Take a nostalgic trip through the websites of the early 00s. + One for animal lovers: sperm whales have teamed up to support a newborn. + Here’s a long overdue answer to a vital question: can the world’s largest mousetrap catch a limousine?