Google’s Gemini AI Has Its Own iPhone App
Google’s Gemini AI Has Its Own iPhone App
It’s the easiest way yet for iOS users to use Google’s AI, and it comes with Gemini Live.
In the AI chatbot world, ubiquity is everything. Companies have raced to build desktop and mobile apps for their bots, in order to both give them new capabilities but also to make sure they’re right in front of your face as often as possible.
There’s no better example of that than Google’s new Gemini app for iPhone, which quietly hit the App Store around the world this week. The free app is simple and straightforward: it’s just a chat window and a list of your previous chats. You can query the bot with text, voice, or your camera, and it’ll give you answers. It’s effectively identical to the Gemini section of the Google app, or what you’d get by opening a browser and going to the Gemini website.
The Gemini app does have one newish feature: access to Gemini Live, the bot’s more interactive and conversational chat mode that is similar to ChatGPT’s voice mode. Gemini Live has been available on Android for a few weeks, but this is the first place it has been usable for iPhone owners. In my short tests so far it works really well, and when you’re using Live it shows up both in the iPhone’s Dynamic Island and on your lock-screen.
More about Google’s new Gemini app for iPhone on The Verge
BT Group, Barclays Investment Bank & NatWest Group On Artificial Intelligence
Deepika Adusumilli, Chief Data & AI Officer, BT Group; Sara O'Keane, Managing Director, Barclays Investment Bank; and Wendy Redshaw, Chief Digital Information Officer, Retail, NatWest Group speak with Bloomberg's Lizzy Burden about how AI is driving innovation and transforming industries at Winning the Innovation Game during the Modernizing IT Without Disruption event in London.
Google Drops New Gemini Model, It Goes Straight To The Top Of The Leaderboard
It's only an experiment at this point. Google is constantly updating Gemini, releasing new versions of its AI model family every few weeks. The latest is so good it went straight to the top of the Imarena Chatbot Arena leaderboard — toppling the latest version of OpenAI's GPT-4o.
Previously known as the LMSys arena, it is a platform that lets AI labs pit their best models against one another in a blind head-to-head. The users vote but don't know which model is which until after they've voted.
The new model from Google DeepMind has the catchy name Gemini-Exp-1114 and has matched the latest version of GPT-4o and exceeded the capabilities of the o1-preview reasoning model from OpenAI.
The top 5 models in the arena are all versions of OpenAI or Google models. The first model on the leaderboard not made by either of those companies is xAI's Grok 2.
More about Google’s latest Gemini LLM on Tom’s Guide
Why The Next AI Breakthroughs Will Be In Reasoning, Not Scaling
There's an ongoing debate about whether AI scaling laws will hold or hit a wall in the near future. However, what's clear now is today's models already have the power to increase productivity in ways that would have been unimaginable just a few years ago.
In this episode of the Y Combinator Lightcone podcast, we dig into the results of a recent o1 hackathon hosted by YC to find out what can be unlocked when founders leverage a SOTA reasoning model.
Unlocking Generative AI’s True Value: A Guide to Measuring Return on Investment
In the race to harness the transformative power of generative AI, companies are betting big – but are they flying blind? As billions pour into gen AI initiatives, a stark reality emerges: enthusiasm outpaces understanding. A recent KPMG survey reveals a staggering 78% of C-suite leaders are confident in Gen AI’s ROI. However, confidence alone is hardly an investment thesis. Most companies are still struggling with what generative AI can even do, much less being able to quantify it.
“There’s a profound disconnect between Gen AI’s potential and our ability to measure it,” warns Matt Wallace, CTO of Kamiwaza, a startup building generative AI platforms for enterprises. “We’re seeing companies achieve incredible results, but struggling to quantify them. It’s like we’ve invented teleportation, but we’re still measuring its value in miles per gallon.”
This disconnect is not merely an academic concern. It’s a critical challenge for leaders tasked with justifying large gen AI investments to their boards. Yet, the unique nature of this technology can often defy conventional measurement approaches.
More about measuring Gen AI’s ROI on Venture Beat
Introduction To AI Agents For Developers
Demystifying AI agents, Googlers Aja Hammerly and Jason Davenport provide a comprehensive overview of their capabilities, applications, and construction.
Join us as we unravel the diverse definitions, explore compelling use cases, and delve into the different architectural approaches for building intelligent agents.
How To Get Started With AI Agents (And Do It Right)
Due to the fast-moving nature of AI and fear of missing out (FOMO), generative AI initiatives are often top-down driven, and enterprise leaders can tend to get overly excited about the groundbreaking technology.
But when companies rush to build and deploy, they often deal with all the typical issues that occur with other technology implementations. AI is complex and requires specialized expertise, meaning some organizations quickly get in over their heads.
In fact, Forrester predicts that nearly three-quarters of organizations that attempt to build AI agents in-house will fail. “The challenge is that these architectures are convoluted, requiring multiple models, advanced RAG (retrieval augmented generation) stacks, advanced data architectures and specialized expertise,” write Forrester analysts Jayesh Chaurasia and Sudha Maheshwari.
So how can enterprises choose when to adopt third-party models, open source tools or build custom, in-house fine-tuned models? Experts weigh in.
More about getting started correctly with AI Agents on Venture Beat
AI Inference: Secret To AI's Superpowers
Explore the world of AI Inference, a game-changing technology that's transforming the way we make decisions and interact with machines.
Martin Keen gets into the basics of AI Inference, including what it is, how it works, and its exciting applications in real-world scenarios.
By leveraging Data-Driven Decision Making, AI Inference enables organizations to make more accurate and informed decisions, leading to improved outcomes and increased efficiency.
Thats all for today, however new advancements, investments, and partnerships are happening as you read this. AI is moving fast, subscribe today to be more informed.