Apple doesn't need fixing, and it doesn't want drama. Its new leader is a "product guy" who bleeds six colors. The brief ceremony introducing the new Macbook Pro this week was as low-key as the presenter, Apple's new CEO John Ternus. He stepped onto the low stage at a Los Angeles event space with zero fanfare, said hi, and queued up a video showing the first touchscreen Mac. Then he released the modest crowd of journalists and creators to get hands-on with the new laptop and roam between eye-popping demos of music producers, filmmakers, photographers, and fashion designers using the laptop.

Maybe Chatbots Shouldn't Get to Say'I' or'Me' MIT professor Sherry Turkle warns that people have become dangerously enmeshed with chatbots. During four decades of studying the effects of interaction with computers, Sherry Turkle has developed a reputation as a careful if critical social scientist who approaches her work with academic rigor and distance. It is a polemic warning that we have reached a state of emergency where our humanity is at risk from overreliance on soulless machines who pose as our tutors, mentors, friends, colleagues, and lovers . Turkle is aiming for the high bar of books --,, --that awaken the public to impending crises. Essentially, this diminutive MIT professor is taking on a multitrillion-dollar industry backed by a government eager to accelerate AI into every corner of our personal and professional lives.

As the housing market stalls, Zillow's CEO sees AI as "an ingredient rather than a threat" that can both help the company protect its turf and reinvent how people search for homes. This will not be a banner year for the real estate app Zillow. "We describe the home market as bouncing along the bottom," CEO Jeremy Wacksman said in our conversation this week. Last year was dismal for the real estate market, and he expects things to improve only marginally in 2026. "The way to think about it is that there were 4.1 million existing homes sold last year--a normal market is 5.5 to 6 million," Wacksman says.

The Only Thing Standing Between Humanity and AI Apocalypse Is Claude? As AI systems grow more powerful, Anthropic's resident philosopher says the startup is betting Claude itself can learn the wisdom needed to avoid disaster. Anthropic is locked in a paradox: Among the top AI companies, it's the most obsessed with safety and leads the pack in researching how models can go wrong. But even though the safety issues it has identified are far from resolved, Anthropic is pushing just as aggressively as its rivals toward the next, potentially more dangerous, level of artificial intelligence. Its core mission is figuring out how to resolve that contradiction. Last month, Anthropic released two documents that both acknowledged the risks associated with the path it's on and hinted at a route it could take to escape the paradox.

Silicon Valley's power brokers spent the past year currying favor with President Trump. Two deadly shootings in Minneapolis are now exposing the price of that bargain. It was November 12, 2016, four days after Donald Trump won his first presidential election. Aside from a few outliers (looking at you, Peter Thiel), almost everyone in the tech world was shocked and appalled. At a conference I attended that Thursday, Facebook CEO Mark Zuckerberg said it was " a pretty crazy idea " to think that his company had anything to do with the outcome.

The Math on AI Agents Doesn't Add Up A research paper suggests AI agents are mathematically doomed to fail. The big AI companies promised us that 2025 would be "the year of the AI agents." It turned out to be the year of AI agents, and kicking the can for that transformational moment to 2026 or maybe later. But what if the answer to the question "When will our lives be fully automated by generative AI robots that perform our tasks for us and basically run the world?" is, like that New Yorker cartoon, "How about never?" That was basically the message of a paper published without much fanfare some months ago, smack in the middle of the overhyped year of "agentic AI." Entitled " Hallucination Stations: On Some Basic Limitations of Transformer-Based Language Models," it purports to mathematically show that "LLMs are incapable of carrying out computational and agentic tasks beyond a certain complexity."

As social robots get more deeply integrated intoour everyday lives, they will be expected to engage in meaningful conversations and exhibit socio-emotionally intelligent listening behaviors when interacting with people. Active listening and backchanneling could be one way to enhance robots' communicative capabilities and enhance their effectiveness in eliciting deeper self-disclosure, providing a sense of empathy,and forming positive rapport and relationships with people.Thus, we developed an LLM-powered social robot that can exhibit contextually appropriate sentiment-based backchannelingand active listening behaviors (active listening+backchanneling) and compared its efficacy in eliciting people's self-disclosurein comparison to robots that do not exhibit any of these listening behaviors (control) and a robot that only exhibitsbackchanneling behavior (backchanneling-only). Through ourexperimental study with sixty-five participants, we found theparticipants who conversed with the active listening robot per-ceived the interactions more positively, in which they exhibited the highest self-disclosures, and reported the strongest senseof being listened to. The results of our study suggest that the implementation of active listening behaviors in social robotshas the potential to improve human-robot communication andcould further contribute to the building of deeper human-robot relationships and rapport.

Full-duplex interaction is crucial for natural human-machine communication, yet remains challenging as it requires robust turn-taking detection to decide when the system should speak, listen, or remain silent. Existing solutions either rely on dedicated turn-taking models, most of which are not open-sourced. The few available ones are limited by their large parameter size or by supporting only a single modality, such as acoustic or linguistic. Alternatively, some approaches finetune LLM backbones to enable full-duplex capability, but this requires large amounts of full-duplex data, which remain scarce in open-source form. To address these issues, we propose Easy Turn, an open-source, modular turn-taking detection model that integrates acoustic and linguistic bimodal information to predict four dialogue turn states: complete, incomplete, backchannel, and wait, accompanied by the release of Easy Turn trainset, a 1,145-hour speech dataset designed for training turn-taking detection models. Compared to existing open-source models like TEN Turn Detection and Smart Turn V2, our model achieves state-of-the-art turn-taking detection accuracy on our open-source Easy Turn testset. The data and model will be made publicly available on GitHub.

Full-Duplex Speech-to-Speech Large Language Models (LLMs) are foundational to natural human-computer interaction, enabling real-time spoken dialogue systems. However, benchmarking and modeling these models remains a fundamental challenge. We introduce FLEXI, the first benchmark for full-duplex LLM-human spoken interaction that explicitly incorporates model interruption in emergency scenarios. FLEXI systematically evaluates the latency, quality, and conversational effectiveness of real-time dialogue through six diverse human-LLM interaction scenarios, revealing significant gaps between open source and commercial models in emergency awareness, turn terminating, and interaction latency. Finally, we suggest that next token-pair prediction offers a promising path toward achieving truly seamless and human-like full-duplex interaction.

Despite significant progress in neural spoken dialog systems, personality-aware conversation agents -- capable of adapting behavior based on personalities -- remain underexplored due to the absence of personality annotations in speech datasets. We propose a pipeline that preprocesses raw audio recordings to create a dialogue dataset annotated with timestamps, response types, and emotion/sentiment labels. We employ an automatic speech recognition (ASR) system to extract transcripts and timestamps, then generate conversation-level annotations. Leveraging these annotations, we design a system that employs large language models to predict conversational personality. Human evaluators were engaged to identify conversational characteristics and assign personality labels. Our analysis demonstrates that the proposed system achieves stronger alignment with human judgments compared to existing approaches.