AI Trends
Track the latest developments and trends in AI
Former Plaud exec raises $26M for AI app that listens to you in the background
One of the core promises of AI is that the more it knows about a person, the better it will be able to assist them with tasks and make appropriate suggestions, which, if you'll believe the tech companies, requires apps and devices to listen, watch, or record users' activity all the time and gather all possible context.
Learning to Play Like a Pro: Adaptive Motion Planning and Tracking for Humanoid Robots
This White Paper provides roboticists and control engineers with a comprehensive overview of how motion planning and motion tracking can be decoupled to preserve professional athletic style in humanoid robots, and why adaptive, speed-aware control is essential for closing the gap between simulation and real-world deployment. What you will learn about: Why combining a professional athlete’s motion style with reliable task performance remains an open challenge in humanoid ball-sports robotics, and
Top 12 AI Live Translation Tools in 2026: Latency, Languages, Pricing
We checked 12 AI live translation tools, including Palabra.ai, Google Live Translate, DeepL Voice, Teams Interpreter and Wordly, against official pages for languages, latency, platforms and pricing. The post Top 12 AI Live Translation Tools in 2026: Latency, Languages, Pricing appeared first on MarkTechPost.
TII Releases Falcon-ASR: 1.6B Arabic Speech Model Hits 20.92% WER
TII has released Falcon-ASR, a 1.6B speech recognition model for Arabic with an Emirati focus. It reports 20.92% average WER on 6 Arabic test sets, but weights and an API are not yet available. The post TII Releases Falcon-ASR: 1.6B Arabic Speech Model Hits 20.92% WER appeared first on MarkTechPost.
Meet Cloudflare Clef-omni: A 30B-A3B Multimodal Decision Model for Audio, Video, Image and Text
Cloudflare released Clef-omni, an Apache-2.0 multimodal decision model built on Qwen3-Omni-30B-A3B. It scores text, images, audio and video in one call for $0.15 per million input tokens, while Clef-flash drops to $0.038. The post Meet Cloudflare Clef-omni: A 30B-A3B Multimodal Decision Model for Audio, Video, Image and Text appeared first on MarkTechPost.
What is Decision 3.0? vLLM Semantic Router’s New Open Decision Models
vLLM Semantic Router released Decision 3.0, 5 Apache-2.0 multimodal decision models from 0.8B to 27B. They answer choice, yes/no and score questions about text and images in one call, without generating text. The post What is Decision 3.0? vLLM Semantic Router’s New Open Decision Models appeared first on MarkTechPost.
Will the HomeView be Apple’s first smart home success?
Over the past couple of years, we’ve seen multiple reports that Apple was working on a new smart home product, but now it seems the product has a name and a launch date.
48 hours to TechCrunch Disrupt 2026 — hear directly from the people building what’s next
Less than 48 hours, TechCrunch Disrupt 2026 starts in San Francisco’s Moscone West. Save up to $100 on your pass before prices increase at the door, plus get 50% on a second pass. Or score other deals.
Can the AI industry persuade data center opponents by getting rid of NDAs?
On Equity, we debate the impact of data center moratoriums and whether the AI industry has a communication problem.
These execs think voice AI hasn’t reached its ChatGPT moment yet
Voice AI often misses important points for its context layer, and causes the whole pipeline to break.
OrcaRouter Releases OrcaCyber Zero 1.5 Cybersecurity Model With 1M Context
OrcaRouter released OrcaCyber Zero 1.5, a gated cybersecurity model with a 1M-token context window. It reports 100% on Cybench and 95.8% on an evaluable CVE-Bench subset, priced at $3.00 / $7.50 per 1M tokens. The post OrcaRouter Releases OrcaCyber Zero 1.5 Cybersecurity Model With 1M Context appeared first on MarkTechPost.
Sakana AI’s LLM Peer Review System Catches 73% of Core-Claim Errors
Sakana AI’s TMLR paper introduces Multi-Layered Review, a 3-agent Claude-based reviewer, and a 1,164-error Contradiction Benchmark. MLR caught 73.43% of core-claim errors, versus 14.81% for the best prior system. The post Sakana AI’s LLM Peer Review System Catches 73% of Core-Claim Errors appeared first on MarkTechPost.