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AI News7 Types of AI Conference Attendees. Which One Are You?

7 Types of AI Conference Attendees. Which One Are You?

5:49 PM IST · August 31, 2026

7 Types of AI Conference Attendees. Which One Are You?

After a decade of running India’s largest AI conference, you start to recognise people. Not individuals, but the types of people who show up year after year. Here are seven of them, and what each should do differently this year.

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Sequoia-incubated Empirik launches with $21M to predict outages before they happen

Sequoia-incubated Empirik launches with $21M to predict outages before they happen

Before joining Sequoia Capital in 2020 as chief digital and information officer,Avon Purispent over a decade running infrastructure at Rubrik and VMware. Three years ago, as large language models began showing their true potential, Puri, alongside another Sequoia IT leader,Sudheer Dhurjati, recognized that AI could help autonomously solve a major challenge for infrastructure engineers — preventing tech outages before they occur. The two tech veterans realized that instead of reacting to system failures after the fact, they could build a tool to predict them. This insight led them to buildEmpirik, a product that tracks system changes and infers their potential ripple effects across the entire infrastructure. Sequoia recognized that Empirik could be a new type of observability tool that helps prevent and resolve incidents. After incubating the startup in 2023, Sequoia recruited former Quantum Metric CPO and Salesforce observability VPKartik Chandrayanaas CEO earlier this year. Empirik announced Tuesday that it is spinning out as an independent company after raising $21 million in seed funding from Sequoia, Canapi, and Alumni Ventures. “There has always been a lot of money spent in keeping systems up and running,” Sequoia partnerBogomil Balkanskytold TechCrunch, adding that most existing observability tools fail to understand complex system dependencies. Empirik, he claims, is one of the first dedicated tools focused on tracking changes across massive environments. It acts as an autonomous “traffic cop” — permitting low-risk changes, setting guardrails on larger ones, and flagging the most dangerous updates for human review. By serving as an autonomous infrastructure engineer, Empirik allows busy DevOps and site reliability engineering teams to offload routine troubleshooting and focus on higher-value priorities. Since launching earlier this year, Empirik has already brought on customers ranging from startups to several Fortune 500 players, including S&P Global, Guardant Health, and a major consumer packaged goods (CPG) company. Empirik’s goal is to do for infrastructure engineers what Cursor and Claude Code did for software developers: automate certain tasks so they can work significantly faster. As AI accelerates the pace of software development, tools that help infrastructure engineers keep up with constant system changes are more vital than ever, Chandrayana told TechCrunch. “What agentic AI did for software, Empirik wants to do for infrastructure engineering,” he added. As for competition, Balkansky claims Empirik is in a category of its own for now, acting as a complementary layer to AI SRE platforms like Resolve and Sequoia-backed Traversal.

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ChatGPT Health adds Epic integration for clinicians to import patient data

ChatGPT Health adds Epic integration for clinicians to import patient data

OpenAI said today that it is integrating ChatGPT Health with Epic’s electronic health record (EHR) system, which holds data for over325 million patients, to let clinicians import patient data and use AI to ask questions. The company said that in certain systems, it will also integrate ChatGPT directly within EHR workflows. With ChatGPT, clinicians can now access information like appointment notes, laboratory results, medications, and specialist documentation and summarize them easily. They can also look at patient history, identify changes, and prepare for future appointments. OpenAI said that in some deployments, clinicians can directly access ChatGPT for getting pre-visit review and building clinical timelines without leaving a patient chart. The company specified that the integration allows only read-only access to health records, and AI doesn’t write anything back. OpenAI is also adding a new Healthcare Public Data plug-in, which can fetch information from sources including ClinicalTrials.gov, CMS Coverage, RxNorm, DailyMed, and PubMed. The company said that the new plug-in can help healthcare workers synthesize data for trial eligibility criteria, medication identifiers, coverage policy versions, and provider records. The company said that apart from enabling these new features, it is allowing organizations with a Business Associate Agreement to use ChatGPT Work, Codex, apps, and connectors in their workspace for compliant workflows. Last month, OpenAI rolled outChatGPT for health to all U.S. consumers. The company said at that time that people are asking 300 million health-related queries to ChatGPT every week. Despite many deep integrations with medical systems, the company has maintained that AI is not suitable for diagnosis or treatment. OpenAI said that it collected over 4,300 responses from physicians across 27 clinical use cases, including pre-visit review, clinical timelines, medication review, and handoff summaries, and found that 99.1% of responses were safe. However, even a few unsafe answers can lead to harmful results for humans. A few days before this rollout, a Florida-based pastor sued the company, alleging thatChatGPT gave him a near-fatal recommendation. The company was also sued in May by family members of a user who blamedChatGPT for giving wrongful advice related to dosage.

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Google’s answer to Canva is an AI tool where you prompt instead of design

Google’s answer to Canva is an AI tool where you prompt instead of design

Google is entering the creative design market with a new image-creation and editing tool called Google Pics, which will become a part of its Google Workspace suite for business customers and premium Google AI subscribers. The company says the product, which is powered by Google’s Nano Banana image-generation model, will roll out over the coming weeks to most Workspace customers and those who pay for Google AI Pro or Ultra. Similar to popular design tools like Adobe Express and Canva, Google’s new product is meant to be used for the sort of everyday design tasks you might come across at work and sometimes in your personal life. For instance, you could use Google Pics to create a poster, social media posts, and other visuals and illustrations. But unlike Canva, where artists, illustrators, and photographers can publish templates, graphics, photos, and other art to a marketplace where they earn royalties, Google Pics is about creating things based on AI, which was trained on artists’ work. And unlike Adobe Express, a tool creators often use to make that work in the first place, Google Pics is a tool where you prompt to create, not where you design something new from scratch. The app also includes other editing tools, like those to isolate and transform objects, modify or translate text inside images, and others. Plus, it supports collaborative editing and the ability to create several different generations of the image you’ve requested, so you can pick the best one. The software will be built into Google Workspace, initially starting with Docs and Slides, which rolls out today. It will later come to Google Drive, the company says.

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Anthropic’s new Fable release is cheaper, less restrictive

Anthropic’s new Fable release is cheaper, less restrictive

On Tuesday, Anthropic released Fable and Mythos 5.1, twinned versions of the company’s most advanced AI model. In addition to performance upgrades, the new Fable release includes changes meant to reduce token cost and false-positive restrictions from the model’s safeguards. As with the previous Mythos model, Mythos 5.1 will only be available to registered Anthropic partners engaged in either cybersecurity or life sciences research. Fable 5.1, the unrestricted version, is available starting today on cloud platforms or through the Anthropic API. One of the most significant changes is Anthropic’spreviously reportedembrace of zero data retention, allowing clients to run Anthropic models on their own infrastructure without data outflows. Previously unavailable for Fable due to security concerns, a high-privacy service (calledEnterprise Frontier Safeguards) will now roll out to users in June. Notably, the system will still monitor for misuse by agents or human users, but clients will control how the monitoring takes place. As part ofthe announcement, Anthropic assured customers that their data had not been inappropriately accessed. “Anthropic has never trained on enterprise data without explicit permission, and never will,” the announcement reads. As is common for an Anthropic release, the new models set records in a range of benchmarks, including Terminal-Bench 4.0 (for CLI-based coding) and Humanity’s Last Exam (for general reasoning). Anthropic also released three novel scientific findings generated by the models before their release, including a custom GPU optimization and a high-resolution map of Venus assembled from existing photos. As with previous releases, the models come witha detailed system card, which explains their capabilities in most straightforward terms. The system card rates Mythos as “low-risk” for concerns related to automated AI development — where the AI improves itself — which some see as a trigger for a loss of human control. It says “its ability to accelerate internal AI R&D progress is in line with current trends.” In terms of general misbehavior, Mythos is slightly more prone to it than Opus, possibly as a result of its enhanced capabilities. “Mythos 5.1 is a slight regression on overall misaligned behavior compared to Opus 5, and an improvement over Mythos 5 and Claude Sonnet 5,” the system card reads. “It cooperates with human misuse and accepts unverifiable claims of authorization somewhat more readily than Opus 5, but it is less likely to ignore explicit constraints, hallucinate inputs, or falsely claim to have completed tasks than previous models.”

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