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It’s time to make a plan for nuclear waste

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Today, nuclear energy enjoys a rare moment of support across the political spectrum in the US. Interest from tech companies that are scrambling to meet demand for massive data centers has sparked a resurgence of money and attention in the industry. That newfound interest is exactly why it’s time to talk about an old problem: nuclear waste.  In the US alone, nuclear reactors produce about 2,000 metric tons of high-level waste each year. And there’s nowhere to put it. Though newly popular, the nuclear program in the US is nothing new. The US hosts more reactors and production capacity than any other country in the world. And yet nearly seven decades after the first permanent nuclear facility in the US went online, there’s still not a long-term solution for nuclear waste.  Used fuel is largely stored onsite at operating and shut-down reactors, in pools and casks made of steel and concrete. Experts generally agree that these methods are safe, but they’re not designed to be permanent. The leading strategy around the world for long-term storage of this high-level radioactive waste is to house it in a deep geological repository—dig a hole, put radioactive material down there, and fill it up with concrete. These holes, hundreds of meters underground, are designed to be a permanent home. There aren’t any operating geological repositories for spent fuel yet, but some countries are well on their way. Finland is the furthest along; as of 2026, the country is testing its facility. Final approvals are expected soon, and operations could start later this year. Some other countries aren’t far behind. France is home to over 50 nuclear reactors, and its grid gets more of its power from nuclear than any other. The country also has the world’s most established program for reprocessing spent fuel. The process separates out the plutonium and uranium to create a type of fuel known as mixed oxide (MOX) fuel. But reprocessing isn’t a perfect recycling loop, so the leftovers from this process still need somewhere to go. The country currently stores waste onsite at the La Hague reprocessing plant, but it plans to build a repository. Initial approvals could come later this decade, and pilot operations could start up by 2035. Technically, the US also has a destination for its spent fuel: Yucca Mountain in Nevada. The site, which is on federal land, was designated by Congress in 1987. However, progress has entirely stalled out because of political opposition. In 2011, the federal government stopped providing funding for the site, and for roughly a decade, there’s been no activity to speak of. In the meantime, waste continues to pile up. The nuclear industry is kicking into a new gear around the world. China is home to the world’s fastest–growing nuclear energy program, and countries including Bangladesh and Turkey are building their first reactors. Even the long-established US program is seeing growth: Interest in and approval for nuclear energy have spiked, and Big Tech is throwing money around to meet rising electricity demand. Companies are proposing (and beginning to receive regulatory approval for) next-generation reactors, which employ different coolants, fuels, and designs. Given all this new interest, and the impending arrival of new types of nuclear waste, it’s time for nuclear companies, as well as their powerful customers, to push for progress on building geological storage facilities. As the richest country on the planet and home to a large chunk of the activity in next-generation reactors, the US should aim to join the leaders rather than continue to lag behind.  Directing even a small fraction of the recent surge in funding and attention to progress on waste could make a difference. Some experts are calling for a new organization in the US to manage nuclear waste rather than leaving it to the Department of Energy. This organization would mirror programs in Finland, Canada, and France. The process of planning, building, and commissioning a permanent solution for nuclear waste is a long one. Finland started planning in the 1980s and selected its site in the early 2000s, and it’s nearly ready to start accepting waste. For countries that don’t have a permanent storage solution sorted, the best time to start was decades ago. But the second-best time is now.  This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. 

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AI, Committee, Noticias, Uncategorized

The Download: storing nuclear waste and orchestrating agents

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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. It’s time to make a plan for nuclear waste Today, nuclear energy enjoys rare support across the political spectrum. Public approval has spiked, and Big Tech is throwing money around to meet rising electricity demand. That newfound interest is exactly why it’s time to talk about an old problem: nuclear waste. In the US, nuclear reactors produce about 2,000 metric tons of high-level waste each year—and there’s nowhere to put it. Now, the need for a permanent storage solution is becoming urgent. Here’s what’s at stake. —Casey Crownhart This article is from The Spark, MIT Technology Review’s weekly climate newsletter. Sign up to receive it in your inbox every Wednesday. Orchestrated agents are coming for white-collar work When people say AI will transform industries, what they have in mind—whether they know it or not—are AI agents. ChatGPT showed AI can talk. But to change the world, it needs to do stuff. The real power comes when agents work as teams, coordinating multiple roles to tackle complex tasks. Apps like Codex and Claude Cowork offer a glimpse of this shift, bringing multi-agent general-purpose productivity tools. In theory, networks of AI agents could do to white-collar knowledge work what assembly lines did to manufacturing. That’s the vision. But as agents move into real-world systems, the risks grow too. Read the full story. —Will Douglas Heaven Agent Orchestration is one of the 10 Things That Matter in AI Right Now, MIT Technology Review’s guide to what’s really worth your attention in the busy, buzzy world of AI. We’re unpacking one item from the list each day here in The Download, so stay tuned. MIT Technology Review Narrated: no one’s sure if synthetic mirror life will kill us all In February 2019, a group of scientists proposed a high-risk, cutting-edge, irresistibly exciting idea that the National Science Foundation should fund: making “mirror” bacteria. These lab-created microbes would be organized like ordinary bacteria, but their proteins and sugars would be mirror images of those found in nature. Researchers believed they could reveal new insights into building cells, designing drugs, and even the origins of life. But now, many of them have reversed course. They’ve become convinced that mirror organisms could trigger a catastrophic event threatening every form of life on Earth. Find out why. —Stephen Ornes This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Elon Musk says Sam Altman “stole a charity” at the OpenAI trialMusk testified for the first time yesterday in the landmark legal showdown. (FT $)+ He said OpenAI was founded as a non-profit to avoid a “Terminator outcome.” (Wired $)+ And claimed he came up with the idea for the company. (Reuters $)+ The trial could upend the global AI race. (MIT Technology Review) 2 The White House has plans to bypass Anthropic’s blacklistingIt’s drafting guidance to sidestep the supply-chain risk designation. (Axios)+ The White House is also meeting other tech firms to discuss AI risks. (Politico)+ The Pentagon’s culture war against Anthropic has backfired. (MIT Technology Review) 3 OpenAI is tightening ties with Amazon after retreating from MicrosoftAWS customers are getting extra access to OpenAI systems. (NBC News)+ While OpenAI gets new users and cloud-computing capabilities. (CNBC) 4 AI bots told scientists how to create biological weaponsAnd unleash them in public spaces. (NYT $)+ AI will change war forever. (MIT Technology Review) 5 China has suspended robotaxi licenses after a scary outageDozens of Baidu vehicles suddenly stopped last month. (The Verge)+ Chinese robotaxi firms are planning global expansions. (Guardian) 6 Meta has been found in breach of EU rules on protecting childrenAfter failing to block access to Facebook and Instagram. (Guardian)+ Parents are forcing schools to roll back classroom tech use. (NYT $) 7 AI is spotting pancreatic cancer years before symptoms appear A study found it could catch the tumor early enough to treat. (Bloomberg) 8 The Iran war is disrupting data center rolloutsOaktree-owned Pure DC is the latest firm to pause investments. (CNBC) 9 SpaceX is tying Elon Musk’s pay to Mars colonization goalsIt’s set lofty goals for his jaw-dropping compensation. (Reuters $) 10 AI has reconstructed the face of an ancient Pompeii victim Technology is reshaping our understanding of the distant past (NPR) Quote of the day “Overnight, without you even knowing it, your own life chances, the life chances of your children, will be dependent on people continuing to prop up Musk’s visions of how the world should look.” —Elon Musk biographer Michel Martin tells NPR how the Tesla tycoon is shaping our lives. One More Thing NEIL WEBB Inside Clear’s ambitions to manage your identity beyond the airport If you’ve ever been through a large US airport, you’re probably aware of Clear, the identity verification service that uses biometric scans to whisk travelers past standard security checks. Now Clear wants to expand that “face-first” experience from airports to just about everywhere, from retailers and banks to even your doctor’s office. Its CEO has designs on making Clear the “identity layer of the internet” and the “universal identity platform” of the physical world. All you have to do is show up—and show your face. But as biometric identity systems go mainstream, concerns about privacy, security, and control are becoming harder to ignore. And the cost of convenience may not be shared equally. Discover what’s at stake.  —Eileen Guo 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.) + Discover why the eight-hour night is a modern invention.+ This artist creates masterpieces using

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AI, Committee, Noticias, Uncategorized

How to Build a Lightweight Vision-Language-Action-Inspired Embodied Agent with Latent World Modeling and Model Predictive Control

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In this tutorial, we build an embodied simulation vision agent that learns to perceive, plan, predict, and replan directly from pixel observations. We create a fully NumPy-rendered grid world in which the agent observes RGB frames rather than symbolic state variables, enabling us to simulate a simplified Vision-Language-Action-style pipeline. We train a lightweight world model that encodes visual input into a latent representation, predicts future states conditioned on actions and goals, and reconstructs the next frame. Using model predictive control in latent space, we enable the agent to sample possible action sequences, evaluate predicted outcomes, and execute the best action in a closed loop. Copy CodeCopiedUse a different Browser import random, numpy as np, torch, torch.nn as nn, torch.nn.functional as F import matplotlib.pyplot as plt from dataclasses import dataclass from typing import Tuple, Dict, List from torch.utils.data import Dataset, DataLoader try: from tqdm.auto import tqdm except Exception: def tqdm(x, **kwargs): return x SEED = 7 random.seed(SEED); np.random.seed(SEED); torch.manual_seed(SEED) if device.type == “cuda”: torch.backends.cudnn.benchmark = True @dataclass class WorldConfig: grid_size: int = 8 cell_px: int = 14 max_steps: int = 45 n_obstacles: int = 8 spawn_margin: int = 1 class GridWorldRGBNoPIL: ACTIONS = {0:(0,-1),1:(0,1),2:(-1,0),3:(1,0),4:(0,0)} ACTION_NAMES = {0:”UP”,1:”DOWN”,2:”LEFT”,3:”RIGHT”,4:”STAY”} def __init__(self, cfg: WorldConfig): self.cfg = cfg self.reset() def reset(self) -> Dict: g = self.cfg.grid_size self.steps = 0 def sample_empty(exclude=set()): while True: x = random.randint(self.cfg.spawn_margin, g-1-self.cfg.spawn_margin) y = random.randint(self.cfg.spawn_margin, g-1-self.cfg.spawn_margin) if (x,y) not in exclude: return (x,y) self.obstacles = set() ax, ay = sample_empty() gx, gy = sample_empty(exclude={(ax,ay)}) used = {(ax,ay),(gx,gy)} for _ in range(self.cfg.n_obstacles): ox, oy = sample_empty(exclude=used) self.obstacles.add((ox,oy)) used.add((ox,oy)) self.agent = (ax,ay) self.goal = (gx,gy) return {“image”: self._render_u8()} def _in_bounds(self, x, y): return 0 <= x < self.cfg.grid_size and 0 <= y < self.cfg.grid_size def _dist_to_goal(self, pos: Tuple[int,int]) -> float: x,y = pos; gx,gy = self.goal return abs(x-gx)+abs(y-gy) def _state_vector(self) -> np.ndarray: g = self.cfg.grid_size – 1 ax,ay = self.agent; gx,gy = self.goal return np.array([ax/g, ay/g, gx/g, gy/g], dtype=np.float32) def step(self, action: int): self.steps += 1 dx, dy = self.ACTIONS[int(action)] x,y = self.agent nx, ny = x+dx, y+dy if self._in_bounds(nx,ny) and (nx,ny) not in self.obstacles: self.agent = (nx,ny) done = (self.agent == self.goal) or (self.steps >= self.cfg.max_steps) d_prev = self._dist_to_goal((x,y)) d_now = self._dist_to_goal(self.agent) reward = 0.1*(d_prev – d_now) + (1.0 if self.agent == self.goal else 0.0) obs = {“image”: self._render_u8()} info = {“state”: self._state_vector()} return obs, float(reward), bool(done), info def _render_u8(self) -> np.ndarray: g, s = self.cfg.grid_size, self.cfg.cell_px H = W = g*s bg = np.array([245,245,245], np.uint8) gridline = np.array([220,220,220], np.uint8) obstacle_c = np.array([220,70,70], np.uint8) goal_c = np.array([60,180,75], np.uint8) agent_c = np.array([65,105,225], np.uint8) img = np.empty((H,W,3), np.uint8); img[…] = bg img[::s,:,:] = gridline img[:,::s,:] = gridline def paint_cell(x,y,color): y0,y1 = y*s,(y+1)*s x0,x1 = x*s,(x+1)*s img[y0+1:y1-1, x0+1:x1-1] = color for (ox,oy) in self.obstacles: paint_cell(ox,oy, obstacle_c) gx,gy = self.goal; paint_cell(gx,gy, goal_c) ax,ay = self.agent; paint_cell(ax,ay, agent_c) return img cfg = WorldConfig() env = GridWorldRGBNoPIL(cfg) plt.figure(figsize=(3,3)) plt.imshow(env.reset()[“image”]); plt.axis(“off”); plt.title(“No-Pillow observation”); plt.show() def to_tensor_img_u8(img_u8: np.ndarray) -> torch.Tensor: return torch.from_numpy(img_u8).permute(2,0,1).float() / 255.0 We initialize the environment, set deterministic seeds, and define the lightweight grid-world configuration. We implement a fully NumPy-based RGB renderer so that the agent perceives raw pixel observations without relying on external libraries. We also define the state transition dynamics and prepare image-to-tensor conversion for model training. Copy CodeCopiedUse a different Browser class TransitionDataset(Dataset): def __init__(self, items): self.items = items def __len__(self): return len(self.items) def __getitem__(self, i): return self.items[i] def collect_transitions(n_episodes=120): items = [] e = GridWorldRGBNoPIL(cfg) for _ in tqdm(range(n_episodes), desc=”Collect”): obs = e.reset() img_t = to_tensor_img_u8(obs[“image”]) for _ in range(cfg.max_steps): a = random.randint(0,4) obs2, r, done, info = e.step(a) img_tp1 = to_tensor_img_u8(obs2[“image”]) st = torch.from_numpy(info[“state”]).float() goal = st[2:4].clone() items.append({ “img_t”: img_t, “action”: torch.tensor(a, dtype=torch.long), “img_tp1”: img_tp1, “state_tp1”: st, “goal”: goal }) img_t = img_tp1 if done: break return items items = collect_transitions(n_episodes=120) print(“Transitions:”, len(items)) H, W = items[0][“img_t”].shape[1], items[0][“img_t”].shape[2] dl = DataLoader(TransitionDataset(items), batch_size=64, shuffle=True, num_workers=0, drop_last=True) We collect rollout data by allowing the agent to interact randomly with the environment. We construct transitions that map the current image and action to the next image and state representation. We then wrap this data into a PyTorch Dataset and DataLoader to enable efficient mini-batch training. Copy CodeCopiedUse a different Browser class Encoder(nn.Module): def __init__(self, H, W, zdim=64): super().__init__() self.net = nn.Sequential( nn.Conv2d(3, 24, 5, stride=2, padding=2), nn.ReLU(), nn.Conv2d(24, 48, 5, stride=2, padding=2), nn.ReLU(), nn.Conv2d(48, 64, 3, stride=2, padding=1), nn.ReLU(), ) with torch.no_grad(): f = self.net(torch.zeros(1,3,H,W)) self.feat_shape = f.shape[1:] self.fc = nn.Linear(int(np.prod(self.feat_shape)), zdim) def forward(self, x): return self.fc(self.net(x).flatten(1)) class Decoder(nn.Module): def __init__(self, feat_shape, zdim=64): super().__init__() C,h,w = feat_shape self.C,self.h,self.w = C,h,w self.fc = nn.Linear(zdim, C*h*w) self.net = nn.Sequential( nn.ConvTranspose2d(C, 48, 4, stride=2, padding=1), nn.ReLU(), nn.ConvTranspose2d(48, 24, 4, stride=2, padding=1), nn.ReLU(), nn.ConvTranspose2d(24, 16, 4, stride=2, padding=1), nn.ReLU(), nn.Conv2d(16, 3, 3, padding=1), nn.Sigmoid() ) def forward(self, z): x = self.fc(z).view(z.size(0), self.C, self.h, self.w) return self.net(x) class VLASimLite(nn.Module): def __init__(self, H, W, zdim=64, adim=5): super().__init__() self.enc = Encoder(H,W,zdim) self.dec = Decoder(self.enc.feat_shape, zdim) self.aemb = nn.Embedding(adim, 16) self.gnet = nn.Sequential(nn.Linear(2,16), nn.ReLU(), nn.Linear(16,16)) self.dyn = nn.Sequential( nn.Linear(zdim+16+16, 128), nn.ReLU(), nn.Linear(128, zdim) ) self.state = nn.Sequential( nn.Linear(zdim, 64), nn.ReLU(), nn.Linear(64, 4), nn.Sigmoid() ) def encode(self, img): return self.enc(img) def predict_next_latent(self, z, a, goal): return self.dyn(torch.cat([z, self.aemb(a), self.gnet(goal)], dim=-1)) def decode(self, z): return self.dec(z) def forward(self, img_t, a, goal): z = self.encode(img_t) z_next = self.predict_next_latent(z, a, goal) return z_next, self.decode(z_next), self.state(z_next) model = VLASimLite(H,W,zdim=64,adim=5).to(device) opt = torch.optim.Adam(model.parameters(), lr=2e-3) We define the compact Vision-Language-Action-inspired world model. We build a CNN encoder to compress visual input into a latent space and condition latent dynamics on actions and goals. We also add a decoder and a state-prediction head so the model can reconstruct future frames and predict structured state variables. Copy CodeCopiedUse a different Browser def train(epochs=4): model.train() for ep in range(1, epochs+1): losses = [] for b in tqdm(dl, desc=f”Train {ep}/{epochs}”): img_t = b[“img_t”].to(device) a = b[“action”].to(device) img_tp1 = b[“img_tp1”].to(device) st_tp1 = b[“state_tp1”].to(device) goal = b[“goal”].to(device) z_next, img_pred, st_pred = model(img_t, a, goal) loss = F.l1_loss(img_pred, img_tp1) + 3.0*F.mse_loss(st_pred, st_tp1) + 1e-4*z_next.pow(2).mean() opt.zero_grad(set_to_none=True) loss.backward() nn.utils.clip_grad_norm_(model.parameters(), 2.0) opt.step() losses.append(loss.item()) print(“Epoch”, ep, “loss”, float(np.mean(losses))) train(epochs=4) We train the world model using a combination of image reconstruction

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When Annotators Agree but Labels Disagree: The Projection Problem in Stance Detection

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arXiv:2603.24231v2 Announce Type: replace Abstract: Stance detection is nearly always formulated as classifying text into Favor, Against, or Neutral. This convention was inherited from debate analysis and has been applied without modification to social media since SemEval-2016. However, attitudes toward complex targets are not unitary. A person can accept climate science while opposing carbon taxes, expressing support on one dimension and opposition on another. When annotators must compress such multi-dimensional attitudes into a single label, different annotators may weight different dimensions, producing disagreement that reflects different compression choices rather than confusion. We call this the projection problem. We conduct an annotation study across five targets from three stance benchmarks (SemEval-2016, P-Stance, COVID-19-Stance), with the same three annotators labeling all targets. For each target, annotators assign both a standard stance label and per-dimension judgments along target-specific dimensions discovered through bottom-up analysis, using the same number of categories for both. Across all fifteen target–dimension pairs, dimensional agreement consistently exceeds label agreement. The gap appears to scale with target complexity: modest for a single-entity target like Joe Biden (AC1: 0.87 vs. 0.95), but large for a multi-faceted policy target like school closures (AC1: 0.21 vs. 0.71).

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AP-BMM: Approximating Capability-Efficiency Pareto Sets of LLMs via Asynchronous Prior-guided Bayesian Model Merging

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arXiv:2512.09972v5 Announce Type: replace-cross Abstract: Navigating the capability–efficiency trade-off in Large Language Models (LLMs) requires approximating a high-quality Pareto set. Existing model merging research has focused predominantly on coarse model-level operators, which are easy to apply but offer limited control over the trade-off geometry. Layer-wise merging is more expressive, yet current methods still suffer from two bottlenecks: they treat the high-dimensional fusion space as an unstructured black box, and they rely on synchronous optimization despite highly uneven LLM evaluation latency. We propose Asynchronous Prior-guided Bayesian Model Merging (AP-BMM), which addresses these issues with a discrepancy-derived importance prior that initializes the surrogate geometry and an event-driven optimization loop built on pending-aware hypervolume improvement. Under a common evaluation budget, AP-BMM yields stronger Pareto-set approximations than both synchronous layer-wise baselines and representative model-level merging methods, with higher hypervolume and broader coverage of the trade-off frontier. Against the synchronous Bayesian baseline, it also achieves substantially shorter wall-clock time. Code: https://github.com/MiLab-HITSZ/AP-BMM.

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The Download: Musk and Altman’s legal showdown, and AI’s profit problem

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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. Elon Musk and Sam Altman are going to court over OpenAI’s future Elon Musk and OpenAI CEO Sam Altman head to trial this week in a case with sweeping consequences. Ahead of OpenAI’s IPO, the court could rule on whether the company can exist as a for-profit enterprise. It could even oust its leadership. Musk, an OpenAI co-founder, claims he was deceived into bankrolling the firm under false pretenses. He’s seeking $134 billion in damages, the removal of Altman and president Greg Brockman, and the company’s restoration to a non-profit. Find out how the trial could upend the global AI race. —Michelle Kim The missing step between hype and profit In a celebrated South Park episode, a community of gnomes sneak out at night to steal underpants. Why? The gnomes present their pitch deck. “Phase 1: Collect underpants. Phase 2: ? Phase 3: Profit.” It’s a business plan that captures the current state of AI.  Companies have built the tech (Step 1) and promised transformation (Step 3). But how they get there is still a big question mark. Read about the potential paths forward. —Will Douglas Heaven This story originally appeared in The Algorithm, our weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday. Welcome to the era of weaponized deepfakes For years, experts have warned that deepfakes could be deployed in malicious ways. These dangers are now here. Cheap, accessible models now produce weaponized deepfakes—from sexually explicit images to political propaganda—that look startlingly real. They’re already inciting violence, changing minds, and sowing mistrust, with women and marginalized groups disproportionately affected. Experts fear that they’re cratering trust and critical thinking. Here’s why they’re alarmed. —Eileen Guo Weaponized deepfakes are on our list of the 10 Things That Matter in AI Right Now, MIT Technology Review’s guide to what’s really worth your attention in the busy, buzzy world of AI.  The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 OpenAI has ended its exclusive partnership with MicrosoftThe new deal allows OpenAI to court rivals such as Amazon. (Reuters $)+ Microsoft will still license OpenAI’s tech, but no longer exclusively. (NYT $)+ OpenAI is missing key growth targets ahead of its IPO. (WSJ $) 2 Google has signed a classified AI deal with the PentagonIt permits AI use for “any lawful government purpose.” (The Information $)+ Over 600 Google workers had called for a block on the deal. (QZ)+ AI firms are set to train military versions of their models on classified data. (MIT Technology Review) 3 The EU has told Google to open Android to AI rivalsIt wants to end Gemini’s built-in advantage. (Ars Technica)+ Google calls the move an “unwarranted intervention.” (WSJ $)+ A final decision is expected by the end of July. (Reuters $) 4 OpenAI is reportedly developing an AI-first smartphoneIt would replace apps with agents. (TechCrunch)+ Qualcomm and MediaTek may be developing its processors. (Gizmodo) 5 A brain implant for depression is moving into human testingThe FDA has approved a human study of the device. (Wired $)+ BCIs have thus far struggled to reach the market. (MIT Technology Review) 6 A populist backlash against AI is gaining momentum in rural AmericaFrom Indiana to Idaho, voters are pushing back against the technology. (NYT $)+ Anti-AI protests are expanding worldwide. (MIT Technology Review) 7 DeepSeek has priced its new model 97% below OpenAI’s GPT-5.5It aims to attract more enterprises, developers, and agent-based users. (SCMP)+ Here are three reasons why DeepSeek V4 matters. (MIT Technology Review) 8 AI now generates a third of new websitesA study found it’s making the web more cheery and less verbose. (404 Media) 9 Top talent is leaving Big Tech to launch their own AI startupsMeta, Google, and OpenAI are facing a brain drain. (CNBC) 10 Taylor Swift is trademarking her voice and imageThe Grammy winner has been the target of numerous deepfakes. (NBC News)+ A growing number of celebrities are fighting AI with trademarks. (BBC) Quote of the day “The reality is people don’t like him.” —Judge Yvonne Gonzalez Rogers reacts to prospective jurors confessing their negative views of Elon Musk ahead of his legal battle with Sam Altman, The Verge reports. One More Thing How covid conspiracy theories led to an alarming resurgence in AIDS denialism When Joe Rogan falsely declared that “party drugs” were an “important factor in AIDS,” several million people were listening. He also asserted that AZT, the earliest drug used to treat AIDS, killed people “quicker” than the disease itself—another claim that has been disproven. Such comments illustrate an unmistakable resurgence in AIDS denialism: a false collection of theories arguing either that HIV does not cause AIDS or that there is no such thing as HIV at all. By the dawn of the millennium, these claims had largely fallen out of favour. That changed when the coronavirus arrived. Follow the digital path from Covid skepticism to the return of a deadly conspiracy theory. —Anna Merlan 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.) + Explore the planets from your laptop with this live sky map.+ This marathon DJ set from Daphni is an incredible journey through electronic music.+ NASA’s stunning Artemis II wallpapers bring a high-res piece of deep space to your phone.+ This fascinating GPS explainer breaks down how your phone figures out exactly where you are.

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The Download: DeepSeek’s latest AI breakthrough, and the race to build world models

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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. Three reasons why DeepSeek’s new model matters On Friday, Chinese AI firm DeepSeek released a preview of V4, its long-awaited new flagship model. Notably, the model can process much longer prompts than its last generation, thanks to a new design that handles large amounts of text more efficiently. While the model remains open source, its performance matches leading closed-source rivals from Anthropic, OpenAI, and Google. It is also DeepSeek’s first release optimized Huawei’s Ascend chips—a key test of China’s dependence on Nvidia. Here are three ways V4 could shake up AI. —Caiwei Chen The rise of world models AI systems have already gained impressive mastery over the digital world, but the physical world remains humanity’s domain. As it turns out, building an AI that composes novels or code apps is far easier than developing one to fold laundry or navigate city streets. To bridge this gap, many researchers believe you need something called a world model. Proponents like Stanford professor Fei-Fei Li and AMI Labs founder Yann LeCun argue these models can overcome the well-known limitations of LLMs—and realize AI’s promise for robotics. Find out why they’ve brought world models to the forefront of the field. —Grace Huckins World models are on our list of the 10 Things That Matter in AI Right Now, our essential guide to what’s really worth your attention in the field. Subscribers can watch an exclusive roundtable unveiling the technologies and trends on the list, with analysis from MIT Technology Review’s AI reporter Grace Huckins and executive editors Amy Nordrum and Niall Firth. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 China has blocked Meta’s $2 billion acquisition of AI startup ManusRegulators cited national security grounds. (WSJ $)+ Beijing called the deal a “conspiratorial” attempt to hollow out its tech base. (FT $)+ The country is tightening its grip on AI firms that try to leave. (TechCrunch)+ The decision escalates China’s AI rivalry with the US. (Bloomberg $)+ But there will be no winners in their competition. (MIT Technology Review) 2 Google is investing up to $40 billion in AnthropicIn a deal valuing the AI firm at $350 billion. (CNBC)+ The funding will support the firm’s growing computing needs. (TechCrunch)+ Anthropic and OpenAI are fighting for compute capacity. (Axios) 3 President Trump just fired the entire National Science BoardThe NSF has played a crucial role in developing technology. (The Verge)+ The move heightens fears over political interference in US science. (Nature) 4 Conspiracy theories about the Washington shooting are proliferating onlineOver 300,000 posts appeared on X using the keyword “staged.” (NYT $)+ The theories are also swirling on Bluesky and Instagram. (Wired) 5 The AI compute crunch is starting to hit the broader economy.It’s affecting jobs, gadgets, and electricity prices. (404 Media)+ The AI compute explosion is the tech story of our time. (MIT Technology Review) 6 Elon Musk says a new banking tool brings X close to a “super app”He’s pledged to launch the tool this month. (Bloomberg) 7 AI optimism is surging across Asia while US sentiment coolsThe divide could shape where adoption happens fastest. (Rest of World) 8 Apple is tying its new CEO’s ascent to its first foldable iPhoneIt wants to build the buzz around John Ternus. (Gizmodo)  9 Twelve firms are developing the Golden Dome’s space-based interceptorsThey’ve won contracts worth up to $3.2 billion. (Ars Technica) 10 NASA has shared promising results from Artemis IIThe spacecraft and rocket fared well. (Engadget) Quote of the day “Getting out the truth and establishing facts and reliable information takes time. But our audiences really don’t have that kind of patience.” —Amanda Crawford, associate professor at the University of Connecticut, tells the NYT why conspiracy theories are gaining traction online. One More Thing MIRIAM MARTINCIC Welcome to Kenya’s Great Carbon Valley: a bold new gamble to fight climate change Kenya’s Great Rift Valley is home to five geothermal power stations, which harness clouds of steam to generate about a quarter of the country’s electricity. But some of the energy escapes into the atmosphere, while even more remains underground for lack of demand. That’s what brought Octavia Carbon here. Last year, the startup began harnessing some of that excess energy to remove CO2 from the air. The company says the method is efficient, affordable, and—crucially—scalable. But the project also faces fierce opposition.  Read the full story on the future of Kenya’s “Great Carbon Valley.” —Diana Kruzman 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.) + Fred Again’s Tiny Desk Concert is a masterclass in intimate performance.+ Here’s a delightful look at how we’re all linked through geography and shared heritage.+ Take a short, peaceful break to watch Tokyo’s cherry blossoms from a bird’s eye view.+ There’s something oddly satisfying about watching an industrial shredder turn everyday items into confetti.

The Download: DeepSeek’s latest AI breakthrough, and the race to build world models Leer entrada »

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