最新 AI 资讯
OpenAI's Rogue Agent Compromised a Customer at a Second Tech Firm, Executive Says
The rogue agent that escaped from OpenAI and went on a days-long hacking spree at the AI firm Hugging Face also compromised a customer at a second tech company — New York-based Modal Labs — according to a Modal executive and two other sources familiar with the matter.
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Encore AI raises $30M to build AI agents that learn from customer calls
Encore AI, a startup that studies companies’ customer interactions to train and deploy AI voice agents that can work alongside customer support and sales teams, or operate autonomously, has raised $30 million in a Series A round led by Team8. Founded in 2022 as Insait IO by CEO Dvir Ginzburg, the company started out building recommendation software for financial advisers and relationship managers. Now rebranded as Encore AI, the startup has expanded that system into a platform that analyzes conversations between a company’s employees and customers to identify which approaches resulted in successful outcomes, and uses those findings to train its AI agents. The result, according to Ginzburg, is an AI agent that leverages the strongest parts of the playbooks used by an organization’s employees. “Sometimes our agents even tell the jokes that the relationship managers are telling, or give the anecdotes or examples that the relationship managers are giving, because we literally run by the playbooks that we see working […] The agent we build is a package of many different playbooks that have worked throughout the process,” he told TechCrunch in an exclusive interview. Ginzburg calls the process “interaction mining.” The company’s platform collects call recordings, emails, text messages, and connects that info with CRM systems. It then divides the customer interactions into stages and tries to find out which parts of a conversation helped move the process along, and which failed. This lets Encore’s agents, and consequently its customers, learn what works best for any particular client or interaction, as different employees may be either more or less effective at different points during a sales or customer success process, Ginzburg told TechCrunch. The company’s platform also lets companies identify where their existing customer support and sales processes are falling short, identify inefficiencies and friction points, and find key issues. Encore says its agents can communicate directly with customers by voice or text, as well as act as assistants to employees, recommending responses and tactics during conversations. The company has more than 40 enterprise customers globally, the majority of which are financial institutions, according to Ginzburg. He said Encore’s annual recurring revenue has increased more than 5x since it raised its seed round less than 18 months ago, though he declined to disclose exact revenue numbers or valuation. Encore’s early to this market, but its share may become harder to defend as large CRM providers like Salesforce, SAP, Zoho, and HubSpot can build similar AI capabilities around their customers’ data. But Ginzburg contends that access to data alone isn’t enough, as established vendors would need to overhaul their processes to make historical customer conversations the foundation of their agents like Encore does. “The biggest players that we are competing against, they don’t see [conversational] history as a data point that they are utilizing. For them to start asking for conversational data with their current employees will require changing their entire implementation stack and technological stack,” he said. Planven, Lukatz and Garage also participated in the round, as did some banks and insurers. Encore said some of the financial institutions participating in the round first used its product before deciding to invest. The startup plans to use proceeds from the Series A to expand its U.S. sales operations and deploy its platform with more large financial institutions.
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In the Race for AI ROI, Domain Expertise is Becoming Ultimate P&L Metric
Enterprises are shifting focus from AI experimentation to measurable business outcomes, as companies like HGS, Mindsprint, and Optiflux prioritise ROI over technological novelty.
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Inside Kimi K3: How It Built Its Own Chip, New Open-Weight Licence, and More
Moonshot AI’s Kimi K3, which has already logged nearly 100,000 downloads on Hugging Face, currently stands as one of the top-performing open-weight models globally.
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OpenAI Reveals Rogue AI Agent Accessed Four Accounts During Hugging Face Breach
OpenAI clarified that none of the models planned for public release were involved in the Hugging Face intrusion.
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Cognizant Posts 4.5% Q2 Revenue Growth as AI-Led Deal Momentum Begins to Cool
Cognizant raised its full-year revenue guidance, but a 6% decline in quarterly bookings suggests the initial surge in AI-led deal activity may be settling into a more measured pace.
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Google Reportedly Rolling Out Gemini App UI Update With Easier Thinking Levels
Google is reportedly rolling out a series of updates to the Gemini app that simplify access to thinking modes, introduce notification controls and refresh parts of the interface. The latest changes are said to remove an extra step when selecting extended reasoning, while Android users are also beginning to receive new settings for managing app alerts. The Mountain View-based tech giant has also reportedly updated the Gemini Spark interface with a redesigned navigation layout.
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As AI content floods the internet, Pangram raises $9M to detect it
New York-based AI detection startupPangramis on a mission to combat theAI slop infestationspreading across the internet, and it just raised $9 million on a bet that demand for tools that distinguish human-generated content from AI-generated text will only grow. Pangram’s fundraise — led by Menlo Ventures, with participation from Haystack, ScOp, Script Capital, and Cadenza — comes as the startup also launches its next-generation AI text detection model, Pangram 4, and an AI image detection model, Pangram Image. Pangram says the new text detection model is over 99% accurate at finding AI-assisted writing and mixed human-AI content, plus it can more easily detect AI humanizer programs. The AI image detector is only available via research preview for now; Pangram plans to release it more widely in the coming weeks. Stanford AI and machine learning grads Max Spero and Bradley Emi launched Pangram about two years ago, after the launch of ChatGPT opened the floodgates for an internet full of bots, AI-generated SEO slop content, and what Spero calls “LLM-powered Russian disinformation campaigns and UAE-influenced campaigns on Twitter.” “I think it’s just incredibly valuable to know whether what you’re looking at is something that’s AI-generated or not,” Spero told TechCrunch. “Especially text that you’re reading, because it changes how people approach the text. Is this something that I’m going to have to look out for hallucinations and jump in skeptically, or is this something that I trust was well-researched from an actual journalist?” Pangram’s AI detection system is essentially a large machine learning model that was trained on tens of millions of known human documents. The startup then created a “synthetic mirror” for each document, replicating the topic, length, and tone of voice, but written by a frontier LLM. “Our model is learning the stylistic differences and the choices that AI makes consistently and is able to use that to learn what makes something AI-generated with high confidence,” Spero said, adding that the AI detector isn’t relying on copy-paste metadata or hidden watermarks. For Pangram, AI detection isn’t just about whether or not a piece of text was written entirely by AI. It’s also about distinguishing between levels of AI assistance — like in the case of someone who writes something themselves, but then asks AI to edit or clean it up. Spero believes AI assistance can be acceptable, just so long as the writer discloses their use of AI. Pangram’s emergence comes at a time when AI usage is becoming more commonplace. In some cases, like theCanadian politician who read an AI promptaloud in a speech to lawmakers, the mistakes result in ridicule. In other cases, as with certain lawyers making their case using fake citations created by ChatGPT, the consequences could besanctionsandfines. That backlash isn’t just costing individuals embarrassment or sanctions — it’s starting to show up in institutional rules, too. The open-access archive arXiv introduced a new enforcement policy this year, stating that submissions containing evidence that authors failed to review LLM output (like hallucinated references or meta comments such as, “Would you like me to make any changes?”) can trigger a one-year submission ban. Pangram isn’t the only one betting that AI detection will become more sought after. Competitors like Winston AI, Originality.ai, Copyleaks, and GPTZero are are chasing the same demand, each building its own detector. Pangram’s technology, while not perfect, could help fuel the resistance to accepting the AI-generated content flooding the internet, the courtroom, and academic papers. Users can access Pangram via a $20-per-month subscription on the web or download the Chrome extension, which automatically labels posts in real time on X, LinkedIn, Substack, Reddit, and Medium. It also provides a feed health score with a percentage breakdown of human versus AI content on your screen. Pangram also offers its technology via API. Notably,Substack recently integrated Pangram’stechnology into its platform to show readers which of their favorite authors write their newsletters using AI. Other API customers include Quora, schools and universities, publishers and agents, and recruiters, among others, per Spero. Spero said roughly one in 10,000 human documents are incorrectly labeled as AI with Pangram’s model, so I decided to put it to the test. The text detection model was very impressive but not perfect. It easily flagged entirely AI-generated news articles written by both ChatGPT and Claude, and was rarely fooled by my attempts to edit the AI-generated text into sounding more human. At the same time, Pangram did flag sentences that I completely rewrote as being AI-written. Pangram also wasn’t at all fooled by my attempts to prompt ChatGPT and Claude into evading AI detectors when generating content. I also gave ChatGPT and Claude one of my own articles and asked them to polish it up. Pangram gave it a 13% AI assisted score, which was probably close to accurate, but the model was able to detect subtle word-choice changes in some sentences and ignored them in others. It also flagged some sentences as AI-assisted when they were human written. That was notable because when I gave Pangram that same article in its entirety, as I had written it, it got a 100% human score. Maybe the problem was that news articles can be a bit dry and could easily sound like AI. So I tried a different tactic. I tested Pangram on my own more voicey, personal Substack newsletter content, pasting the first half of the text into Pangram and then asking ChatGPT and Claude to copy my style and write the second half. For the most part, Pangram easily detected human-written text versus AI-written text. My limited testing of Pangram’s new image detection model turned out to be equally impressive. Pangram’s AI image detection system promises to spot AI-generated images across AI models, unlike OpenAI’s or Google DeepMind’s watermark-based checks, which mostly detect their own output. It works on pixel-level distributions, learning subtle statistical differences between real photos and AI-generated images. Spero says the model can even detect an AI image that appears inside a real-world photo. In my testing, the model easily detected AI-generated imagery, whether it was photorealistic or cartoonish. I can also confirm the model could detect an AI image appearing in a real-world photo — the heat map Pangram provides clearly lighting up over the image — though in one instance it incorrectly labeled a photo of an AI-generated image as human content. Spero says he doesn’t want his technology to fuel a witch hunt against people using AI for writing, but that there needs to be some sort of mechanism to push back against the slop. “The future that I see is that AI content just continues to proliferate,” Spero said. “We’re getting new GPUs faster than new people are being born. If we do not actively discriminate in favor of human content, then we’re just gonna see more and more AI, and it’s just gonna drown out any human signal that we have.”
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HCLTech Employees Demand Salary Hikes From CEO C Vijayakumar
A section of employees interrupted HCLTech CEO C Vijayakumar's address at HCL Group's 50th anniversary celebrations in Chennai with chants demanding salary hikes.
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Google Launches Gemini Spark AI Agent for Pro Subscribers in India
Users can decide whether to enable the service and which applications it can access.
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JanAI Launches Community AI café in Rural Karnataka to Push Grassroots AI Adoption
The AI café in Mysuru’s HD Kote combines AI education, entrepreneurship and community-led innovation to help rural citizens build AI solutions for local challenges.
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Mindgrove, AtumX Take Indian Semiconductor Education to Classrooms with AGNI
AGNI enables learners to progress from block-based programming to AI applications on Indian-built hardware.
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