Category: AI News

[ad_1] Training AI reasoning models demands resources that most enterprise teams do not have. Engineering teams are often forced to choose between distilling knowledge from large, expensive models or relying on reinforcement learning techniques that provide sparse feedback. Researchers at JD.com and several academic institutions recently introduced a new training paradigm that sidesteps this dilemma.…

[ad_1] When people talk about artificial intelligence, they usually focus on what it produces: Human-like text, stunning images, or eerily accurate recommendations. What rarely gets attention is how AI understands anything in the first place. That understanding begins with encoders. Think of an encoder as a translator that converts messy, real-world information into a structured…

[ad_1] 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…

[ad_1] AI R&D runs on a cycle of hypothesis, experiment, and analysis — each step demanding substantial manual engineering effort. A new framework from researchers at SII-GAIR aims to close that bottleneck by automating the full optimization loop for training data, model architectures, and learning algorithms. A new framework called ASI-EVOLVE, developed by researchers at…

[ad_1] A billion dollars in startup funding for a company that employs 12 people is an indication that investors still have faith in AI. But the founder of the startup in question – AMI Labs’ Yann LeCun – believes that the breed of technology we currently term AI (large language models) is not the way…

[ad_1] As AI agents move from research demos to production deployments, one question has become impossible to ignore: how do you actually know if an agent is good? Perplexity scores and MMLU leaderboard numbers tell you very little about whether a model can navigate a real website, resolve a GitHub issue, or reliably handle a…

[ad_1] AI systems are increasingly built around data that does not really pause. Financial markets are an obvious example, where inputs keep updating, not arriving in fixed batches. In that kind of setup, something like the BNB price stops being a single figure and starts to look more like a stream that keeps changing. Cryptocurrency…

[ad_1] For years, the computer vision community has operated on two separate tracks: generative models (which produce images) and discriminative models (which understand them). The assumption was straightforward — models good at making pictures aren’t necessarily good at reading them. A new paper from Google, titled “Image Generators are Generalist Vision Learners” (arXiv:2604.20329), published April…

[ad_1] During Operation Lunar Peek in November 2024, attackers gained unauthenticated remote admin access — and eventual root — across more than 13,000 exposed Palo Alto Networks management interfaces. Palo Alto Networks scored CVE-2024-0012 at 9.3 and CVE-2024-9474 at 6.9 under CVSS v4.0. NVD scored the same pair 9.8 and 7.2 under CVSS v3.1. Two…

[ad_1] To stop automation waste, enterprises must deploy interaction infrastructure that physically governs how independent AI agents operate. AI agents now populate corporate networks, reasoning through tasks and executing decisions with increasing autonomy. Yet, when these independent actors attempt to coordinate work, exchange context, or operate across varied cloud environments, the interaction framework degrades quickly.…