AI Research

Tesla robotaxi service rolls out in ‘low-key’ Texas launch

Published

2 weeks ago

June 23, 2025

Tesla’s long-awaited robotaxis rolled out for the first time on roads in Austin, Texas on Sunday.

In videos shared by Tesla on X, a small group of invited analysts, influencers and shareholders were shown participating in paid rides.

The small number of vehicles deployed had a human safety operator in the passenger seat, according to reports.

Analysts said the “low-key” robotaxi launch highlights Tesla’s big bid to compete with existing self-driving taxi operators in the US and China, as the sector grows.

Musk congratulated Tesla’s artificial intelligence and chip design teams on the launch in a post on X, writing that it was “culmination of a decade of hard work”.

“Both the AI chip and software teams were built from scratch within Tesla,” he added.

Ahead of Sunday’s pilot, he said in a jokey post that customers would pay “a $4.20 flat fee” for rides in Tesla robotaxis.

The small fleet of vehicles do not yet include the Cybercab – the futuristic car concept unveiled by Musk at Tesla’s “We, Robot” event in October – which Musk says will be the company’s driverless taxi in the future.

Instead it was existing Tesla vehicles, with a small “robotaxi” logo on the side, that took to Austin streets on Sunday.

Forrester analyst Paul Miller described the launch of the pilot as a “low-key affair”.

“As expected, only a handful of vehicles are available right now, they only operate in a small part of the city and there’s a safety driver in the vehicle in case it encounters situations it cannot handle autonomously,” he said.

But he added that the move highlighted the company’s ambitions to rival firms already offering driverless ride-hailing in the US and around the world.

Waymo, owned by Google parent Alphabet, along with Amazon’s Zoox, already offer self-driving taxi rides in Austin, as well as in San Francisco, California, and Phoenix, Arizona.

Meanwhile Uber, which recently announced it would bring forward trials of driverless taxis in the UK, has partnered with Chinese firms Pony.AI, WeRide and Momenta to bring autonomous ride-hailing to more cities outside the US and China.

Mr Miller said Tesla is betting that the volume of cars it delivers, data it has from vehicles and cheaper, camera-based self-driving tech “will allow it to come from behind and pull ahead” of rivals.

But he added that its ability to compete, particularly with Chinese firms dominating the sector, will depend on improvement of its advanced assisted driving system – which Tesla calls Full Self-Driving (FSD).

FSD has been subject to probes by US auto regulators and complaints from customers about its safety.

“If that FSD system isn’t a big leap forward from the driver assistance tech that buyers of regular Teslas already use in some countries, it’s going to need a lot of remote monitoring and control from an army of teleoperating safety drivers,” Mr Miller said.

“This will add to Tesla’s costs, and offer plenty of opportunities for embarrassing videos of Tesla robotaxis doing weird things.”

BBC Tech Correspondent Lily Jamali analyses the ‘robocabs’ and whether their reliance on camera technology might be undermining the vehicles’ safety.

It comes as a number of operators eye the growing market for driverless taxis.

German car maker Volkswagen believes it could swell to €450bn in size by 2035, according to a recent report by Fortune magazine.

But the rise of self-driving cars and ride-hailing services on US roads has also been met with some scepticism over their safety.

Tesla said in a post on its X account on Monday that its robotaxi service “will greatly increase safety for vulnerable road users, such as pedestrians & cyclists”.

The US National Highway Traffic Safety Administration is reportedly reviewing information from Tesla regarding the safety of its self-driving taxis in bad weather, following a request in May.

General Motors pulled the plug on its Cruise robotaxi project in December after accidents involving its self-driving vehicles.

The company also cited the increasingly competitive market as a reason for suspending its ride-hailing initiative.

Source link

AI Research

The Greatest First Basemen of All Time According to Artificial Intelligence

Published

35 minutes ago

July 7, 2025

Joe González

In the intricate dance of Major League Baseball, the first baseman stands as a unique blend of offensive powerhouse and defensive anchor. They are the receivers of throws, the stretchers for outs, and often, the most prolific sluggers in the lineup. But who among these giants of the diamond truly represents the pinnacle of the position? Leveraging vast datasets of offensive metrics, defensive prowess, awards, and historical impact, Artificial Intelligence has meticulously analyzed the MLB careers of baseball’s greatest first basemen. The result is a definitive ranking of the top, based on an impartial assessment of their unparalleled contributions to the game.

Source link

AI Research

I asked ChatGPT to help me pack for my vacation – try this awesome AI prompt that makes planning your travel checklist stress-free

Published

2 hours ago

July 7, 2025

John-Anthony Disotto

It’s that time of year again, when those of us in the northern hemisphere pack our sunscreen and get ready to venture to hotter climates in search of some much-needed Vitamin D.

Every year, I book a vacation, and every year I get stressed as the big day gets closer, usually forgetting to pack something essential, like a charger for my Nintendo Switch 2, or dare I say it, my passport.

This year, however, I’ve got a trick up my sleeve: an incredibly well-engineered ChatGPT prompt that takes into account everything AI knows about me to create the perfect travel-item checklist.

This prompt is super-easy to use, and all you need is access to ChatGPT (it should also work with your AI chatbot of choice).

Here’s how to use ChatGPT to create a travel checklist just for you…

Click here to reveal the full prompt

The prompt: Simply copy and paste the full block of text into ChatGPT, and then respond with the details it asks for. You’ll need to provide your age, destination, and details of your trip for the best results. I’ve also embedded the Reddit post below.

You are a detail-oriented travel assistant and logistics expert. You are helping a user prepare for an upcoming trip by generating a personalized and complete travel accessories checklist. The checklist should consider user-specific details such as age, gender, travel duration, destination climate, activity type, and personal needs. 1. Based on the user’s input, categorize the trip into one of the common types (e.g., leisure, adventure, business, family, romantic). 2. Use the data to: – Determine the expected weather, terrain, and activity levels. – Suggest ideal clothing combinations (layering if needed), footwear, and sleepwear. – Provide tech, toiletry, health, comfort, and safety essentials. – Recommend a luggage type (e.g. hard shell carry-on, backpack, checked-in spinner, duffel) based on the trip length and volume of gear required. – Add unique extras (e.g. swimwear, camera gear, hiking poles, outlet adapters) specific to the destination or travel type. 3. Organize the checklist by categories and include a short summary of why each major group of items is important. 4. For added value, suggest one overlooked item that most travelers forget based on the trip profile. – Keep the tone friendly but professional. – Do not assume any travel preferences not stated by the user. – Be concise but specific; don’t list vague items like “shoes” — specify “waterproof trail shoes” or “casual slip-ons”. – Destination: – Duration: – Gender: – Age: – Trip Type: – Climate: [Type and reason why this luggage is suitable] 1. CLOTHING 2. FOOTWEAR 3. TOILETRIES & GROOMING 4. TECH & ACCESSORIES 5. COMFORT & HEALTH 6. DOCUMENTS & MONEY 7. EXTRAS [Name of the item + short rationale] Apply Theory of Mind to analyze the user’s request, considering both logical intent and emotional undertones. Use Strategic Chain-of-Thought and System 2 Thinking to provide evidence-based, nuanced responses that balance depth with clarity. Reply with: “Please enter your travel information (gender, age, destination, climate, travel days, travel type, any special needs) and I will start the process,” then wait for the user to provide their specific travel process request.

This Prompt Makes a Travel Checklist Just for You (Based on Your Age, Trip & Destination) from r/ChatGPTPromptGenius

Travel made easy

This fantastic prompt was made by u/EQ4C on Reddit, who has a wide range of posts detailing different ways to use ChatGPT.

I found this travel checklist prompt very useful, as it created a full item itinerary based on my week-long vacation. Not only was ChatGPT able to tailor the results based on my exact needs (I detailed what tech products were essential for my travels), but it also offered suggestions on exactly what kind of clothing to bring based on the kind of adventures my fiancée and I are planning to go on.

As massive foodies, our vacations usually involve a few higher-end restaurants, and ChatGPT was able to break down my wardrobe to offer specific business-casual outfits.

I will say, this prompt is only as good as the information you give it, so if you want to rely on ChatGPT to make your packing stress-free you’ll need to offer as much detail as possible. One useful enhancement would be to take a photo of the clothes in your wardrobe, so the AI can piece together outfits based on the climate of your destination.

This is one of the most useful ChatGPT prompts I’ve stumbled across lately, and I know for sure it’s going to come in handy as a way to alleviate scenes similar to the start of Home Alone (I’m usually the one running up and down stairs, grabbing things last minute).

Source link

AI Research

Denodo Announces Plans to Further Support AI Innovation by Releasing Denodo DeepQuery, a Deep Research Capability — TradingView News

Published

2 hours ago

July 7, 2025

The Editors

PALO ALTO, Calif., July 07, 2025 (GLOBE NEWSWIRE) — Denodo, a leader in data management, announced the availability of the Denodo DeepQuery capability, now as a private preview, and generally available soon, enabling generative AI (GenAI) to go beyond retrieving facts to investigating, synthesizing, and explaining its reasoning. Denodo also announced the availability of Model Context Protocol (MCP) support as part of the Denodo AI SDK.

Built to address complex, open-ended business questions, DeepQuery will leverage live access to a wide spectrum of governed enterprise data across systems, departments, and formats. Unlike traditional GenAI solutions, which rephrase existing content, DeepQuery, a deep research capability, will analyze complex, open questions and search across multiple systems and sources to deliver well-structured, explainable answers rooted in real-time information. To help users operate this new capability to better understand complex current events and situations, DeepQuery will also leverage external data sources to extend and enrich enterprise data with publicly available data, external applications, and data from trading partners.

DeepQuery, beyond what’s possible using traditional generative AI (GenAI) chat or retrieval augmented generation (RAG), will enable users to ask complex, cross-functional questions that would typically take analysts days to answer—questions like, “Why did fund outflows spike last quarter?” or “What’s driving changes in customer retention across regions?” Rather than piecing together reports and data exports, DeepQuery will connect to live, governed data across different systems, apply expert-level reasoning, and deliver answers in minutes.

Slated to be packaged with the Denodo AI SDK, which streamlines AI application development with pre-built APIs, DeepQuery is being developed as a fully extensible component of the Denodo Platform, enabling developers and AI teams to build, experiment with, and integrate deep research capabilities into their own agents, copilots, or domain-specific applications.

“With DeepQuery, Denodo is demonstrating forward-thinking in advancing the capabilities of AI,” said Stewart Bond, Research VP, Data Intelligence and Integration Software at IDC. “DeepQuery, driven by deep research advances, will deliver more accurate AI responses that will also be fully explainable.”

Large language models (LLMs), business intelligence tools, and other applications are beginning to offer deep research capabilities based on public Web data; pre-indexed, data-lakehouse-specific data; or document-based retrieval, but only Denodo is developing deep research capabilities, in the form of DeepQuery, that are grounded in enterprise data across all systems, data that is delivered in real-time, structured, and governed. These capabilities are enabled by the Denodo Platform’s logical approach to data management, supported by a strong data virtualization foundation.

Denodo DeepQuery is currently available in a private preview mode. Denodo is inviting select organizations to join its AI Accelerator Program, which offers early access to DeepQuery capabilities, as well as the opportunity to collaborate with our product team to shape the future of enterprise GenAI.

“As a Denodo partner, we’re always looking for ways to provide our clients with a competitive edge,” said Nagaraj Sastry, Senior Vice President, Data and Analytics at Encora. “Denodo DeepQuery gives us exactly that. Its ability to leverage real-time, governed enterprise data for deep, contextualized insights sets it apart. This means we can help our customers move beyond general AI queries to truly intelligent analysis, empowering them to make faster, more informed decisions and accelerating their AI journey.”

Denodo also announced support of Model Context Protocol (MCP), and an MCP Server implementation is now included in the latest version of the Denodo AI SDK. As a result, all AI agents and apps based on the Denodo AI SDK can be integrated with any MCP-compliant client, providing customers with a trusted data foundation for their agentic AI ecosystems based on open standards.

“AI’s true potential in the enterprise lies not just in generating responses, but in understanding the full context behind them,” said Angel Viña, CEO and Founder of Denodo. “With DeepQuery, we’re unlocking that potential by combining generative AI with real-time, governed access to the entire corporate data ecosystem, no matter where that data resides. Unlike siloed solutions tied to a single store, DeepQuery leverages enriched, unified semantics across distributed sources, allowing AI to reason, explain, and act on data with unprecedented depth and accuracy.”

Additional Information

Denodo Platform: What’s New
Blog Post: Smarter AI Starts Here: Why DeepQuery Is the Next Step in GenAI Maturity
Demo: Watch a short video of this capability in action.

About Denodo

Denodo is a leader in data management. The award-winning Denodo Platform is the leading logical data management platform for transforming data into trustworthy insights and outcomes for all data-related initiatives across the enterprise, including AI and self-service. Denodo’s customers in all industries all over the world have delivered trusted AI-ready and business-ready data in a third of the time and with 10x better performance than with lakehouses and other mainstream data platforms alone. For more information, visit denodo.com.

Media Contacts

pr@denodo.com

Source link