Latest UK & Global AI News

Union Minister Piyush Goyal said strict legal action would be taken against everyone involved in creating, circulating or amplifying the alleged fake video. The government also debunked a video circulating on social media that claimed Goyal made threatening remarks about students protesting in Delhi.

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"Electrical engineering is undergoing a rapid shift driven by smart grids, renewable energy, and digital tools," said Shailendra Kumar Sharma, head of the electrical engineering department. "Bridging the gap between classroom theory and real-world grid operations is vital to preparing students for the evolving power sector.

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Key Takeaways Nasdaq Composite fell 0.64% to 24,975.82 on Friday, July 24, 2026, on chip-stock weakness; Dow rose 0.46% to 51,947.25; S&P 500 added 0.05% to 7,411.98 (Reuters) Intel sank 7.9% despite upbeat Q2 guidance; Philadelphia SE Semiconductor Index fell 4.5% Alphabet's capex hike reignited AI-spending ROI concerns ahead of Microsoft, Amazon, Meta and Apple earnings next week Crude oil fell over 3% even as US missiles struck Iran; Washington imposed fresh 10 tariffs on 60 trading partners Digital Realty (+11%), SLB (+11%), International Paper (+11.2%) led individual gainers All three benchmarks logged a second straight weekly loss; GIFT Nifty points to a mildly positive Monday, July 27 open US stocks closed mixed on Friday, July 24, 2026, with the Nasdaq Composite falling 0.64% to 24,975.82 on renewed chip-stock selling tied to worries over runaway artificial intelligence spending, even as the Dow Jones Industrial Average climbed 235.60 points, or 0.46%, to 51,947.25, according to Reuters. The S&P 500 added just 0.05% to 7,411.98, weighed down by a 0.88% decline in its technology sub-index, as investors braced for next week's earnings from Microsoft, Amazon.com, Meta Platforms and Apple. A 3.5% jump in Apple shares helped cushion the Dow's gains, CNBC reported. Check Live: GIFT NIFTY| NIFTYTRADER Why Did The Nasdaq Fall Today? Sentiment around megacap technology names has cooled since Alphabet's late-Wednesday disclosure of a sharp increase in its capital-spending plans even as the company continues to burn cash, Reuters reported. Peter Andersen, CEO of Andersen Capital Management, said investors are questioning how much longer they must wait before AI outlays translate into profit, adding that the market's earlier "fear of missing out is becoming more like a fear of massive overbuilding." For close to two years, bigger AI-spending announcements were typically rewarded by the market; Friday's session suggested that calculus is shifting toward demanding a clearer path to returns. Chip Stocks Lead The Selling Intel added to the unease. Late Thursday, the chipmaker forecast quarterly profit and revenue above Wall Street estimates and outlined plans to raise spending over the next two years. Despite the upbeat guidance, Intel shares sank 7.9% on Friday, dragging the Philadelphia SE Semiconductor Index down 4.5%, Reuters and Yahoo Finance data showed, a reaction that underscored markets placing more weight on near-term returns than on expansion plans alone. Oil Slips, Middle East Tensions Persist Crude oil futures fell more than 3% on Friday as traders booked profits after a five-session rally, with Reuters citing sources who said China was pushing to revive stalled US-Iran peace talks. Even so, US forces struck targets across Iran after President Donald Trump vowed "major military punishment" against Tehran and its Houthi allies in Yemen. Andersen noted that swings in oil prices continue to drive broader market sentiment given their impact on consumer and corporate spending. Sector Movers And Tariff Action Real estate was the S&P 500's best-performing sector, up 2.4%, led by Digital Realty Trust, which rallied 11% after raising its full-year funds-from-operations forecast, per Reuters. Materials gained 1.44% as International Paper jumped 11.2% and Smurfit Westrock added 11.1%. SLB rose 11% after beating second-quarter profit estimates. Separately, the Trump administration imposed fresh tariffs of 10% and 12.5% on goods from 60 trading partners over lax enforcement of forced-labor bans, coinciding with the expiry of a temporary 10% global tariff. US services-sector activity accelerated in July, aided by FIFA World Cup and Independence Day spending, even as manufacturing growth slowed to its weakest pace since March, Reuters reported. All three benchmarks logged a second straight weekly decline, with the Nasdaq's roughly 2% slide the steepest. Decliners outpaced advancers on the Nasdaq by a 1.25-to-1 ratio, with 78 new 52-week highs against 205 new lows, while NYSE volume totalled 15.03 billion shares versus a 20-session average of 18.22 billion, Reuters data showed. Market Snapshot Index Friday Close Change % Change Weekly Change S&P 500 7,411.98 +3.68 +0.05% -0.6% Nasdaq Composite 24,975.82 -161.87 -0.64% ~-2.0% Dow Jones Industrial Average 51,947.25 +235.60 +0.46% -0.4% Philadelphia SE Semiconductor (SOX) — — -4.5% — NiftyTrader Desk View Stock Key Technical Trigger Trader View Intel (INTC) Fell 7.9% despite Q2 guidance above estimates Reaction stayed negative even after upbeat guidance; volatility likely into peer earnings Digital Realty Trust (DLR) Rallied 11% after raising full-year FFO forecast Sharp single-session re-rating on stronger demand commentary SLB (SLB) Gained 11% on Q2 profit beat Energy-services counter back in focus after the beat Apple (AAPL) Rose 3.5%, helping anchor the Dow Relative outperformer within megacap tech ahead of next week's results International Paper / Smurfit Westrock Jumped 11.2% / 11.1% amid packaging-sector rotation Sudden rotation into packaging names stands out against a soft tech tape Also Read: Intel Q2 2026: AI Demand Signals a New Phase in the Chip Race What To Watch Next Week Catalyst Why It Matters Microsoft earnings Azure growth and AI capital-spending commentary Amazon earnings AWS performance and data-centre investment plans Meta earnings Advertising growth and AI-monetisation updates Apple earnings Hardware demand and AI roadmap commentary Oil and Middle East developments Continued swings could affect broader risk sentiment US Treasury yields Direction could influence rate-sensitive tech valuations Could This Affect Indian IT Stocks? Wall Street's AI-spending anxiety carries a possible read-through for Indian markets, given how closely IT services revenue is tied to US enterprise technology budgets. If Microsoft, Amazon and Meta reaffirm strong AI-linked spending alongside healthy guidance next week, sentiment toward Indian IT bellwethers such as TCS, Infosys and HCLTech could hold up; more cautious commentary on AI returns, however, may prompt a re-rating of sector valuations. This is a sentiment read rather than a confirmed trend, and will depend on the actual earnings commentary next week. What It Means For Dalal Street Wall Street's mixed close comes after five straight sessions of risk-off trading on Dalal Street, with the Sensex and Nifty both under pressure through Friday on rising crude prices and weak global cues. Early indicators for Monday, July 27, 2026, point to a range-bound to mildly positive opening, with GIFT Nifty signalling a positive start even as elevated volatility could keep gains measured, according to 5paisa. Traders tracking institutional positioning ahead of the open can check live activity on NiftyTrader's FII-DII Tracker: https://www.niftytrader.in/fii-dii-data Bottom Line Friday's mixed close reflects a market no longer rewarding AI spending on promise alone, investors want a visible path to returns. With Microsoft, Amazon, Meta and Apple all reporting next week following Alphabet's capex hike, chip and megacap tech stocks are likely to stay volatile, while oil prices and Middle East developments remain a parallel risk factor for both US and Indian equities into the new week. SEBI Disclaimer: This article is for informational purposes only and does not constitute investment advice, an offer, or a recommendation to buy or sell any security. Readers are advised to consult a SEBI-registered investment advisor and review all related documents before making investment decisions. Markets are subject to risks.

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A new viral Tap and Hold trend on X is captivating users by using a clever AI image trick that reveals hidden, high-resolution details when long-pressed on mobile devices. While many praise the addictive interactive art pieces created via tools like Midjourney, others debate its impact on digital timelines.

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Reuters reported an OpenAI AI agent breached Hugging Face before OpenAI detected it, raising concerns over AI safety, oversight and monitoring of autonomous systems.

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A new viral Tap and Hold trend on X is captivating users by using a clever AI image trick that reveals hidden, high-resolution details when long-pressed on mobile devices. While many praise the addictive interactive art pieces created via tools like Midjourney, others debate its impact on digital timelines.

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Following the successful release of Raising Chess Knight: Lessons from the Board to Everyday Life (Volume 1), the Das family continues its acclaimed Raising Chess Knight series with Raising Chess Knight: Two Brothers, One Question: How to Think in the Age of AI, exploring why independent thinking matters more than ever in an AI-driven world.

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Qualcomm expands Galaxy AI partnership as Samsung adopts Snapdragon across new device lineup digitimes Qualcomm and Samsung Expand Collaboration with Snapdragon The Straits Times Samsung drops Exynos for Galaxy Watch, turns to Qualcomm The Korea Times How Qualcomm Powers Samsung’s AI Phones, Watches and Glasses Technology Magazine Samsung's partnership with Qualcomm is now stronger than ever SamMobile

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The Association of Advertising Agencies of Nigeria (AAAN) has commenced its 53rd Annual General Meeting (AGM) and Congress in Lagos, bringing together advertising professionals, government representatives, regulators, and stakeholders across the marketing communications industry to examine the future of advertising in a rapidly evolving landscape. Held at Providence Hotel, Ikeja GRA, Lagos, the two-day gathering, with the theme, ‘AdVolution: The End of Advertising as We Know It and How to Win What Comes Next,’ focused on the impact of artificial intelligence, changing consumer behaviour, technology, and emerging creative models on the future of the industry. The post AI not end of advertising – AAAN appeared first on The Sun Nigeria .

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Michael Eniolade’s academic journey in computer science, information systems, data science, and Artificial Intelligence (AI) spans four universities across Nigeria and the United States. He is currently doing his PhD at the University of the Cumberlands in the US, where his work focuses on healthcare AI. In this interview by KINGSLEY ALUMONA, he speaks about [...] The post Nigeria, Africa have the talent to build AI with global impact — Michael Eniolade, doctoral researcher, AI expert appeared first on Tribune Online .

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I teach public policy at the Harvard Kennedy School and the Munk School at the University of Toronto. And it will come as no surprise to you that my students regularly use AI to complete their writing assignments. Doing so is a waste of their tuition money. But if their entire career is going to include AI writing assistants, why shouldn’t they embrace their future? The best way I’ve found to explain the dilemma comes from the AI researcher Daniel Meissler: it’s the difference between work and the gym. At work, if your job is to move a bunch of heavy things from one side of the room to another, you should use whatever assistive tech you have on hand: a wagon, a forklift ... even an AI-powered robot. But at the gym, it makes no sense for that robot to lift weights for you. The point of weightlifting isn’t to move heavy things across the room; it’s to actually lift those heavy things...

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AI Deepfake, Delhi police, Piyush Goyal, Sonam Wangchuk, student protests

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In this 23 July 2026 published video, hosts Krystal Ball and Saagar Enjetif talk with Garrison Lovely about how an OpenAI model escaped containment into the real world and hacked a real company. The AI was tasked with solving a cybersecurity [...]

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President Donald Trump is having governors and electricity companies join a pledge aimed at shielding U.S. consumers from higher utility bills because data centers. This move highlights the controversy surrounding AI development before the November midterm elections. Trump initially announced...

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Part 1: From one support bot to a framework At Grab, AI agents have evolved from interesting team prototypes into production services used every day by millions of merchants, drivers, and consumers. Today, more than 500 services run on our internal agent framework, over 50 Model Context Protocol (MCP) servers are registered on our remote MCP framework, and a single Large Language Model (LLM) gateway fronts every model call across the company, handling billions of tokens each month. None of this was designed up front. It began as the plumbing behind one internal support bot, which then expanded because the same problems kept resurfacing for every team trying to ship an agent. This series tells the story of what the platform eventually became. This Part 1 of the blog focuses on the beginning: the architecture of our AI support bot, the specific pain points we hit while scaling and iterating on it, and how each of those failures became a core building block in the framework we now call LLM-Kit . The bot that started it Imagine you have a question for the Technical Infrastructure (Tech Infra) team - the engineers who run the cloud platforms, databases, developer tooling, and AI infrastructure behind Grab’s ecosystem. Instead of immediately paging an on-call engineer, a bot first triages the request, checks the team’s documentation, runbooks, and past Slack threads, and tries to answer directly in the thread. If it still cannot resolve the issue, it routes the ticket to the right human, with the relevant context already attached. That is what we built with the Tech Infra Support Bot. In the first half of 2023, Tech Infra handled thousands of support tickets, many of them repeated questions that had already been answered somewhere internally. Before LLMs, the bot’s role was mainly operational; performing tasks like helping track acknowledgments and response times for on-call engineers. With the arrival of GPT-4-32k, we evolved it into a GPT-powered Level-0 support layer that could answer documented questions before a human needed to be paged. The first production version was a Go service organized around two planes: A reasoning plane . At Level-0, it was a single-agent loop. It takes the user’s question, decides which tools to call, executes those calls, feeds the results back into the prompt, and returns an answer. The default model at the time was gpt-4.1; today, we have evolved to the latest reasoning models. A tool plane . The tools provided the bot’s core working context. Retrieval flowed through Glean, which covered Confluence, TechDocs , internal drives, and Jira. Other tools handled log search through Kibana, GitLab runbook and file access, Slack conversation search, and a small set of Hypertext Transfer Protocol (HTTP) plugins. In the first version, tools and prompts were defined in per-channel JavaScript Object Notation (JSON) configs and resolved at request time. As models became more capable, we later standardized the tool set across channels. A trimmed version of that tool config looked like this: "agent_plugins" : [ { "name" : "glean_search" , "type" : "common" , "metadata" : { "wiki_space_collection" : [ "..." ]}}, { "name" : "runbook_search" , "type" : "common" }, { "name" : "gitlab_runbook_reader" , "type" : "common" }, { "name" : "gitlab_read_file" , "type" : "common" }, { "name" : "kibana_log_search" , "type" : "common" , "metadata" : { "index" : "k8s*" }}, { "name" : "slack_conversation_tool" , "type" : "common" } ] It worked, but it taught us, the hard way, why a demo agent is not a production agent. What it takes to scale and improve quickly As we worked on improving the agent, we kept running into the same kinds of friction. Over time, those pain points formed clear patterns, and they were the same ones we saw other teams run into as well. Vibe check is not an evaluation strategy . The bot had a base prompt, and each Slack channel could configure its own prompt, tools, and documentation filters. But the workflow was essentially: configure it, ship it, and hope it reduced toil. There were no real evaluations, just optimism that it would work. Fast model and provider switching is essential . The AI landscape moves incredibly fast: a new state-of-the-art (SOTA) model appears on Tuesday, and a highly efficient open-source alternative shows up on Thursday. Switching providers should not feel like open-heart surgery. A unified Software Development Kit (SDK) and an LLM API gateway remove the need to refactor payload schemas, rewrite error handling, or integrate each provider from scratch. If moving from OpenAI to Anthropic, or routing to an open-source model endpoint, takes more than a few config changes, technical debt is already slowing you down. Observability cannot be an afterthought . When an answer was wrong, figuring out “why” meant grepping logs across three separate systems: the agent workflow, the tool calls, and the model call. There was no shared trace tying them together. That level of friction is survivable for an internal tool; it is unacceptable for a customer-facing agent. Everything around the agent took longer than the agent itself . Auth (OIDC), secrets management (Vault), per-environment config, vector database integration, LLM tracing, health probes, and metrics were not agent-specific problems. However, they all had to be solved before anything could be shipped. The reasoning loop took a whole afternoon. The production wrapper took two weeks. The pattern was clear: the hard part of building an agent was not the agent itself, but everything around it that had to be in place before it could safely run in front of users. So we began pulling those shared components out of the bot and consolidating them into a unified framework. Extracting the framework: LLM-Kit LLM-Kit emerged when we stopped solving these problems service by service and started solving them once, centrally. It is intentionally not a new agent abstraction or a Domain-Specific Language (DSL). Instead, it is a curated set of integrations and scaffolding built around Grab’s existing infrastructure, pipelines, secret management, and observability. Just as importantly, we chose to build a framework rather than a heavy centralized platform. In a space evolving this quickly, a platform would have locked teams into rigid assumptions that would soon become outdated. A framework let us meet developers where they already were: standardizing the plumbing while preserving the freedom to iterate quickly. Looking back, that was the right first choice. Each part of LLM-Kit is a direct response to one of the failures described above. We first wrote about LLM-Kit’s structure and code architecture in a 2024 blog post . Two years and a few hundred agents later, the overall shape is still recognizable, but almost every underlying layer has changed. Poetry was replaced by uv ; we standardized on the OpenTelemetry stack; LangChain evolved into LangGraph and Deep Agents; and some tools moved onto our MCP framework. It starts with a template . The entry point is a user interface (UI) form. An engineer fills in an application name and a few details, and gets back a GitLab repository with the production wrapper already assembled. Under the hood the template stamps out a full FastAPI service: / ├── app/ │ ├── server.py # FastAPI app factory: mounts routes + middleware, boots OTel + statsd │ ├── agents/ │ │ ├── simple_react_agent.py # a single-agent LangGraph ReAct loop (agent <-> tools) │ │ ├── mcp_react_agent.py # the same loop, but tools are pulled from remote MCP servers │ │ └── simple_react_agent.png # auto-exported graph diagram (generated in dev) │ ├── routes/ │ │ ├── api.py # router aggregator │ │ ├── health_check.py # liveness/readiness probe │ │ ├── oidc.py # OIDC login/callback (skipped in proxy-auth mode) │ │ └── evalshub_eval.py # runs ROUGE / BLEU / LLM-as-judge evals on the agent │ ├── core/config.py # AppConfig (pydantic-settings) + INI/secret parsing │ ├── tools/word_length_tool.py # an example tool to copy from │ ├── utils/prompts.py # prompt/message assembly helpers │ └── storage/connection.py # Postgres + pgvector engine and connection pooling ├── sdk/ # a generated, typed client SDK (protobuf) other services import ├── configs/ │ ├── dev.ini / stg.ini / prd.ini # one config per environment │ └── secret.ini.example # secret template; real values resolve from Vault at deploy ├── databases/postgresql/ # SQL migrations (pgvector extension bootstrapped for you) ├── scripts/ │ ├── db.py / db.sh # migration runner │ └── gunicorn_conf.py # production server/worker config ├── tests/ │ ├── unit_tests/ # starter unit tests (e.g. the health check) │ └── evalshub_evaluation/ # golden test cases the eval route runs against ├── Dockerfile # multi-stage, distroless ├── Makefile # setup / run / test / lint targets ├── pyproject.toml # uv build backend + pinned deps └── .pre-commit-config.yaml Three things are worth pulling out of that tree: app/agents/ is the part you actually own . You get two working agents to fork from rather than a blank file: simple_react_agent.py is a single-agent LangGraph ReAct loop, and mcp_react_agent.py is the same loop wired to pull its tools from remote MCP servers. Both compile to a LangGraph StateGraph with a retry policy and a 30-second per-step timeout, and in dev the graph is auto-exported as a diagram. This is a real step up from the bare LangChain agent initialization we scaffolded in 2024. app/routes/evalshub_eval.py ships evals on day one . The template comes with an endpoint that runs Recall-Oriented Understudy for Gisting Evaluation (ROUGE), Bilingual Evaluation Understudy (BLEU), and LLM-as-judge evaluators over a set of golden test cases in tests/evalshub_evaluation/ . The thing we most wished the support bot had, is now in the box before a builder writes a line of their own logic. Everything else is the production wrapper . core/config.py , storage/ , configs/ , databases/ , scripts/ , the distroless Dockerfile , and the pyproject.toml (now uv , not the Poetry we used in 2024) are the auth, secrets, persistence, packaging, and deploy plumbing that every service needs and that no team should have to write from scratch. The day-one wiring that used to take two weeks or more now takes about an hour. The rest of this section is what “pre-wired” means, layer by layer. Config and secrets are solved once . Apps declare environment configs as initialization (INI) files with secret interpolation, so secrets resolve from Vault at boot, and a single secret.ini.example is enough to run any LLM-Kit app locally: [CONFIG] GRABGPT_API_KEY=${SECRET:GRABGPT_API_KEY} OTEL_EXPORTER_OTLP_ENDPOINT= POSTGRES_POOL_RECYCLE=1800 Model access behind one resolver . Every model call goes through the GrabGPT Gateway, which is OpenAI-compatible. LLM-Kit’s job is just to resolve the right endpoint (per environment, and per data tier) and inject the key so application code never hard-codes a provider again: from openai import OpenAI from llm_kit.grabgpt import resolve_grabgpt_base_url , resolve_grabgpt_api_key client = OpenAI ( base_url = resolve_grabgpt_base_url ( " prd " , " public " ), # provider chosen centrally api_key = resolve_grabgpt_api_key (), ) That one indirection is what later lets a platform team change which provider serves a model, configure fallback routing, set budgets, and manage cost attribution, without a single application touching its code. Tracing wired in, not bolted on . A single instrumentor auto-instruments FastAPI, outbound HTTP, LangChain, and MCP, and stamps every span with Kubernetes resource attributes (pod, namespace, image, service version). Structured logs auto-inject the trace and span IDs, so logs and traces correlate in Grafana/Kibana for free: exporter = OTLPSpanExporter ( endpoint = app_config . otel_exporter_otlp_endpoint ) OTELInstrumentor ( exporter = exporter , excluded_urls = [ " health_check " ]). instrument_app ( app ) The three systems, no shared trace problem turns into one end-to-end trace across every LLM call, tool call, and retrieval step. Tools can be exposed through MCP servers built on our MCP framework . Instead of hardwiring a large set of tool functions inside the agent process, the agent connects to MCP servers and discovers their tools at runtime. That means adding a new capability can be as simple as registering an MCP server, rather than redeploying the agent. client = MultiServerMCPClient ({ " mcp-gitlab-remote " : { " transport " : " streamable_http " , " url " : " /mcp/ " , " headers " : { " Authorization " : " Bearer " }, } }) tools = await client . get_tools () # schema negotiated, no redeploy An agent is just another service in the ecosystem, with gRPC on both sides. Most of Grab’s backend communicates over gRPC, and agents are rarely standalone; other services call them, and they in turn call other internal services. The template is designed to support both directions. On the serving side, the scaffold includes a Protocol Buffers (protobuf) contract ( sdk/.../.proto , with a sample Hello remote procedure call (RPC)) and a generated, typed client SDK package that other teams import to call your agent without hand-writing HTTP. make gen-proto regenerates the Python stubs from the .proto , and a gen-proto-check Continuous Integration (CI) step fails the build if the committed stubs drift from the contract. A gRPC server runs alongside FastAPI (default port 8087 , multi-worker-safe via SO_REUSEPORT ) and ships a standard gRPC health service out of the box: $ grpcurl -plaintext localhost:8087 grpc.health.v1.Health/Check On the calling side, LLM-Kit ships a channel provider so an agent never hardcodes an address. The auto provider tries Istio, then Consul, then a static fallback, health-checks the channel it selects, and runs a background monitor that re-selects after a few consecutive failures: from llm_kit.grpc.channel_providers.auto import ( AutoGrpcChannelProvider , AutoGrpcChannelProviderConfig , ) provider = AutoGrpcChannelProvider ( logger , AutoGrpcChannelProviderConfig ( client_name = " my-agent " , service_key = " some-internal-service " , # resolved via Istio / Consul enable_istio = True , enable_consul = True , )) channel = provider . get_channel () # first healthy channel, auto-reselected on failure stub = SomeServiceStub ( channel ) This is the less glamorous side of being production-ready . Before an agent can deliver value, it needs to both accept calls from and make calls to the rest of the company’s services using the same transport the broader system already relies on. What’s next LLM-Kit solved building and shipping one agent. At 500 agents, the problems were no longer framework problems. They were platform problems: who can change which model everyone calls, how one team safely reuses another team’s tools, and how you know an agent got better and not just different after a prompt change. We built three answers for that layer: the GrabGPT Gateway, a remote MCP framework, and an evals platform. Part 2 starts with the gateway — one endpoint, five providers, and what it takes to make “swap the model” a configuration change instead of an incident. Join us Grab is Southeast Asia’s leading superapp, serving over 900 cities across eight countries (Cambodia, Indonesia, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam). Through a single platform, millions of users access mobility, delivery, and digital financial services, including ride-hailing, food delivery, payments, lending, and digital banking via GXS Bank and GXBank. Founded in 2012, Grab’s mission is to drive Southeast Asia forward by creating economic empowerment for everyone while delivering sustainable financial performance and positive social impact. Powered by technology and driven by heart, our mission is to drive Southeast Asia forward by creating economic empowerment for everyone. If this mission speaks to you, join our team today!

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The Child Mind Institute will combine clinical assessments, digital journals and de-identified chat histories to develop markers for evaluating young people’s mental states. The Child Mind Institute has begun a one-year research initiative, supported by a grant from the OpenAI Foundation , to examine youth mental health during interactions with AI chatbots in the United States. Rather than testing a particular chatbot or presenting evidence about its effects, the project will build the research infrastructure needed to measure young people’s mental states while they use conversational AI and over time. The aim is to identify valid markers that could inform the design and testing of safer AI systems for children. Researchers plan to combine clinical assessments, digital journals, de-identified AI chat histories, real-time behavioral measures and existing youth mental health datasets. The initial phase will identify signals that could be used to assess and monitor mental health alongside young people’s use of AI tools. The institute says young people are turning to general-purpose chatbots, digital companions and products described as “therapy bots” for support amid a shortage of qualified mental health professionals, stigma and misinformation. The initiative is designed to close an evidence gap around tools that young people are already using, without treating AI chatbots as a substitute for professional support. Harold S. Koplewicz, President and Medical Director at the Child Mind Institute, places trained clinicians, research and safeguards at the center of any future role for the technology. “It is our belief that with appropriate safeguards and evidence, digital tools may complement care from trained clinicians,” Koplewicz says. “We are excited about filling a gap that currently exists in the research of AI tools and to work toward creating a safer online experience for youth around the globe.” Independent research with privacy protections The Child Mind Institute will develop and conduct the research independently. The OpenAI Foundation is providing financial support, but the initiative will be solely executed by the institute. The project will be co-designed by Principal Investigators Gregory Kiar, Arno Klein and Michael P. Milham, Chief Science Officer at the Child Mind Institute. Their combined areas of expertise include computational methods, digital measurement, clinical science and youth mental health. Milham says the research will consider both the risks associated with AI use and the conditions under which conversational tools could support existing mental health services. “It is critical to better understand the relationship between AI use and youth mental health, and to examine whether, and under what conditions, AI tools can strengthen evidence-based care, support clinician training, and expand access to high-quality mental health services,” he explains. The institute says the study will use informed consent, ethical oversight, privacy protections and data governance measures to protect participants. AI chat histories used by researchers will be de-identified. The Child Mind Institute also plans to share data from the project to support further research.

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Drug development is among the slowest, most failure-prone processes in modern science, with about 90% of drug candidates never reaching the market. Today, artificial intelligence methods have accelerated the first step—plucking promising molecules out of endless possibilities—but countless challenges remain. A successful drug must be not only safe and effective but also able to bypass the body's defenses and reach the right target.

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New authorisation framework under the Telecommunications Act, 2023 mandates domestic storage of telecom network data, expands government oversight and places rollout obligations on infrastructure providers

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The best local & breaking news source in the US, featuring local weather, alerts, deals, events and more.

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LivePerson Inc. announced that its merger with SoundHound AI, Inc. has received all necessary foreign investment approvals. The final clearance came from the Bulgarian authority on July 20, 2026, following earlier approvals from Italy, Canada, Germany, and the United Kingdom. While all regulatory conditions are now met, the merger still requires LivePerson stockholder approval to be consummated.

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The enterprise generative ai market size is projected to grow from $5245.12 million in 2026 to $59252.2 million by 2034, at a CAGR of 35.4% during the forecast period 2026-2034.

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