Mentioned in 100 AI use cases across 25 industries
This is like an ultra-detailed 3D CAD tool for molecules, powered by AI. Instead of engineers designing car parts, RosettaFold3 designs and predicts how proteins, DNA, and small‑molecule drugs fit and move together inside the body.
This is like giving every shopper their own smart personal assistant that knows the entire store, all the promotions, and the shopper’s preferences, and can guide them from “I have a need” to “order placed” through natural conversation across web, app, or even voice.
This is like putting a smart security camera on all your insurance transactions. It watches events in real time, spots suspicious patterns that look like fraud, and alerts your team before money goes out the door.
Think of NVIDIA BioNeMo as a set of very smart chemistry and biology "co-pilots" that can read and write molecules and proteins the way ChatGPT reads and writes text. Instead of scientists manually trying out millions of possibilities in the lab, BioNeMo helps them design and screen promising drug candidates on a computer first, massively narrowing the search space.
This is like giving an insurer a living, zoomable map of how cars and drivers behave in the real world, updated in near real time, and then using AI to spot risks, opportunities, and patterns that humans would never see by looking at tables and static reports.
Imagine a huge classroom where different versions of Google’s Gemini sit side‑by‑side answering the same homework and exam questions. A panel of judges then scores which Gemini answers are most helpful for students. This paper is about building that classroom arena and seeing how good Gemini really is as a learning assistant.
Think of this as building your own ‘Netflix-style’ recommendation brain: it watches what each user does, learns their tastes, and then uses a mix of traditional recommendation models and modern generative AI to decide what to show or suggest next.
This is like having a smart assistant watch all your videos and automatically create a searchable index of what’s said, who appears, where logos show up, and key moments—so teams can quickly find and reuse the right clips without manually scrubbing through footage.
This is like a super‑smart search and monitoring engine for banks and financial firms that can instantly scan all their data (transactions, logs, customer activity, documents) to spot risks, fraud, and opportunities, then plug into AI tools for answers and automation.
This is like upgrading an insurer’s old spreadsheet-based risk calculator to a smart assistant that not only predicts which policies are risky more accurately, but also clearly explains which customer or policy features drove each prediction.
This is like putting a smart security guard in your cloud data center who never sleeps, learns what “normal” looks like, and automatically flags or blocks suspicious behavior before it turns into a breach.
This is like a smart weather-and-power crystal ball: it looks at recent weather and production data and uses machine learning to predict how much solar and wind power will be generated in the next few hours.
Think of a smart city as a city with a digital nervous system. AI is the brain that helps it see traffic jams, power usage, crime hotspots, and public service demand in real time, then quietly adjusts lights, signals, and services to keep everything running smoother and safer.
This is like a super-accurate 3D blueprint generator for molecules inside the body. Instead of running long, expensive lab experiments to see how proteins and potential drugs fit together, AlphaFold 3 uses AI to predict those shapes on a computer in hours, so scientists can shortlist the best drug ideas much faster.
This is like having a smart digital sales associate that quietly watches how people browse, search, and compare products across apps and websites, then helps brands put the right message or product in front of the right shopper at the right time as they move from “just looking” to “I’m ready to buy.”
Think of this as a field guide to all the ways computers can learn from medical and pharma data—like a tireless junior doctor and data analyst rolled into one—to help spot diseases earlier, pick better treatments, and run hospitals and clinical trials more efficiently.
Imagine your streaming app as a smart host at a party who learns what each guest likes, suggests the right music and games at the right moment, and nudges people before they leave so they stay longer and have more fun. This system uses AI to do that automatically for every user in your mobile entertainment app.
This is like an AI-powered microscope that can guess the 3D shape of a protein from its recipe (amino-acid sequence) without needing months of expensive lab work.
This is like giving every student their own smart tutor that learns how they learn, adjusts lessons and exercises to their pace, and gives teachers a dashboard to see who needs what help—automatically.
This is like giving eDiscovery and litigation support teams a super-smart research assistant that can read huge piles of documents, understand what they say, and answer questions about them in plain English—without replacing the lawyers’ judgment.
Think of modern AI in schools as a super-smart homework helper and writing coach that students can use at any time. It can draft essays, solve math problems, and explain concepts in plain language—sometimes so well that it’s hard to tell what work is the student’s and what work is the AI’s.
This is like giving city traffic planners a supercharged crystal ball: AI watches patterns from cameras, sensors, and crash data to predict where and when roads are most dangerous, then suggests fixes such as changing signal timing, speed limits, or enforcement focus.
This is like giving your existing code to a very smart assistant and asking it to write the unit tests for you. The large language model reads the code, guesses what it should do, and then writes test cases to check that behavior automatically.
This is like a smart, conversational tour guide for Washington, DC’s open data. Instead of downloading spreadsheets and decoding columns, any resident or city staffer can just ask questions in plain English—“Where are the most traffic crashes?” or “How many affordable housing units were built last year?”—and the AI finds, summarizes, and explains the relevant data.
Think of this as a super-smart teaching assistant that can instantly create practice questions, explain hard concepts in simpler words, draft lesson plans, and give students personalized feedback 24/7.
This is like giving every scientist in a pharma or biotech lab a tireless, super-fast research partner that can read millions of papers, spot hidden patterns in data, and suggest the next best experiment — while the human still makes the final judgment calls.
This is like giving your entire image and video library a smart brain, so it can automatically understand what’s inside every piece of content and instantly surface the right clips or images for any campaign, channel, or audience.
This is like giving the electric grid a very smart traffic controller that can predict and reroute power flows in real time so lights stay on and renewable energy is used more efficiently.
This is like giving your company’s videos and images a smart librarian who can instantly find any clip or picture based on what’s inside it (people, objects, actions, scenes), even if no one ever tagged or labeled the files correctly.
Think of this as giving every journalist a smart digital assistant that can help research, draft, fact‑check, and personalize stories at scale—while editors stay in control of what gets published.
Imagine every time you open your TV, there’s a smart concierge who has watched everything you’ve ever seen, remembers what you liked, what you quit after 5 minutes, what you binged in a weekend, and what people like you enjoy. That concierge quietly rearranges the shelves so the things you’re most likely to love are always right in front of you. That’s what a Netflix-style recommender system does—at software scale for millions of viewers.
This is like hiring millions of super-fast digital editors who watch everything posted on a social network in real time—hiding abusive or illegal content, flagging rule‑breaking posts, and deciding what to show in people’s feeds based on their interests.
This is like having an always-on creative studio that can instantly draft ad copy, images, videos, and campaign ideas on demand, then refine them based on performance data.
This describes how modern social platforms use AI as an always‑on assistant that decides what each person sees, when they see it, and how brands can talk to them—so every user’s feed and every ad feel custom‑made.
This is like putting GPS trackers on every marketing touchpoint (ads, emails, events) so you can finally see which ones actually helped move a customer from first click all the way to revenue, not just who happened to be last in line.
Think of AI in games as a super-fast assistant concept artist or junior designer: it can draft levels, story ideas, or graphics in seconds, but it still needs a human game designer to decide what’s fun, meaningful, and on-brand.
Think of AI in marketing as a team of tireless digital interns that watch every interaction your customers have with your brand and then help your marketers decide: who to talk to, what to say, when to say it, and on which channel—automatically and at massive scale.
Imagine every student having a patient, expert tutor who is available 24/7, remembers what they know, explains things in many ways, and can instantly create new practice problems and feedback—powered by ChatGPT‑like technology instead of a human.
Like giving every online shopper their own smart in-store salesperson who knows the catalog, can answer questions, suggest outfits, and guide them to the right products in real time.
This is a forward-looking overview of how AI will change digital marketing—like a roadmap showing how smart tools will increasingly help marketers target the right people, create content, run ads, and measure results with far less manual work.
This is about using AI as a super-smart control center for factories and supply chains. It watches machines, inventory, orders, and logistics in real time, then predicts problems before they happen and suggests the best way to run production so you waste less time, material, and money.
Think of this as a super-watchful digital guardian angel for banks. It constantly looks at payments, credit decisions and customer behavior to spot anything risky or suspicious in real time – much faster and more accurately than human teams alone.
This is like giving the mobile network its own team of smart digital engineers who constantly watch how it’s performing, spot problems early, and automatically fix or optimize things before customers notice.
This is like hiring a 24/7 digital concierge and receptionist that chats with your guests on your website, apps, or messaging channels, answering questions, taking bookings, and handling common requests automatically.
Imagine every shopper having a smart helper that knows sales, products, and your preferences, and can do the comparing, searching, and asking-customer-service-questions for you before you ever talk to a human or visit a store.
This is like an intelligent flight simulator for radiologists in training: instead of just reading textbooks, learners practice on realistic imaging cases while an AI tutor adapts to their level, points out what they missed on the scans, and helps them learn faster and more safely before treating real patients.
This is like a super-smart TV ad matcher that watches the show in real time, figures out what it’s about and who is likely watching, and then picks the most relevant ad to show that viewer – without needing their name or cookies.
This is like a smart accountant for your marketing budget: it watches all your ads and customer touchpoints and figures out which ones actually convinced people to buy, so you know where your money is really working.
This is like giving your online store a smarter salesperson who understands spoken questions (voice search) and photos (visual search), then guides shoppers to exactly what they want so they’re more likely to buy.
Think of this as a playbook for law firms and in‑house legal teams on how to safely and productively use tools like ChatGPT: where they help (drafting, summarising, research), where they’re risky (confidentiality, hallucinations), and what changes in culture and process are needed so lawyers actually adopt them.
This is like giving every college student a 24/7 smart study coach that can explain concepts in simple terms, quiz them, and help them plan their learning, rather than just giving them another digital textbook.
Imagine your whole supply chain—factories, warehouses, trucks, and suppliers—running like a smart GPS for your business. It constantly checks traffic (demand), fuel (inventory), and roadblocks (disruptions) and then suggests the best route and timing so you deliver on time with less waste and lower cost.
This is like a smart template wizard for documents: you tell it what kind of document you need and some details, and an AI writes a first draft for you that you can then review and edit.
Think of this as a smart thermometer for customer feelings. It reads reviews, tweets, and comments at scale and tells you whether people are happy, angry, or worried about your products and brand.
Imagine a 24/7 security guard for your telecom network who has read every past fraud case, watches all current activity in real time, and can explain in plain language why something looks suspicious and what to do next. That’s what generative AI brings to fraud prevention: it doesn’t just flag ‘weird’ behavior, it also helps investigate, summarize, and respond to it much faster.
Think of this as a global field guide to “AI-as-a-junior-lawyer”: it surveys how tools like ChatGPT-style assistants, contract analyzers, and legal research bots are being used in law firms and in‑house teams around the world, and what that means for cost, risk, and competitiveness.
This is like an assistant that instantly drafts personalized cold sales emails for you. You tell it who you’re targeting and what you’re selling, and it turns that into ready-to-send email templates you can tweak instead of writing from scratch.
Think of this as a team of always-on smart assistants for an insurance company: one that drafts and reviews policies, one that answers customer questions, one that reads long claim files and medical reports, and one that helps underwriters and actuaries make sense of mountains of data.
Think of this as a super-fast, tireless junior claims adjuster. It reads claim documents, pulls out all the important details, checks rules, and drafts decisions or next steps so your human team only needs to review the tricky edge cases.
Think of this as turning your marketing team into pilots of a self-driving ad machine: humans set goals and guardrails, while AI continuously tests, tweaks, and reallocates budget across channels to get you more customers for less money.
Think of this like an autopilot for your online ads. Instead of humans constantly tweaking budgets, audiences, and creatives, AI watches performance in real time and automatically shifts spend to what works best so you get more sales for every advertising dollar.
Think of AI in programmatic advertising as a super-fast trading bot for ad space: it constantly scans who is online, what they’re doing, and in a split second decides which ad to show, at what price, and on which website to maximize your marketing results automatically.
Think of these AdTech AI agents as a team of tireless digital interns that understand ads, audiences, and campaign data. You tell them your goals (e.g., ‘get more app installs in Germany within this budget’), and they continuously research options, tweak settings, buy media, test creatives, and report back—without needing a human to click every button in every platform.
Imagine your marketing department had an endlessly energetic assistant that could draft ads, personalize messages for every customer, test which versions work best, and adjust campaigns on its own while your team focuses on strategy. That’s what generative AI is doing for marketing and advertising.
Think of AI code assistants as a smart co‑pilot sitting next to every developer: they read what you’re typing, suggest the next few lines or whole functions, explain confusing code, and help spot bugs — much like autocomplete on steroids for programming.
This is like having a smart digital marketing assistant inside Facebook and Instagram that automatically builds and optimizes your ads so more of the right people see them, for less money, with less manual tweaking.
This is like giving your social media team a smart assistant that studies your followers’ behavior all day, figures out what they like, and then helps you decide what to post, when to post it, and who to show it to so your ads and content work better with less guesswork.
This is about using AI as a smart digital marketing assistant that creates, tests, and optimizes your online ads automatically so you sell more without manually tweaking every campaign.
Think of this as a tireless creative and analytics assistant that can draft campaigns, personalize messages for each customer, and learn from results to do better next time—all in minutes instead of weeks.
This is like turning a farm into a ‘smart factory’ for crops and livestock: sensors measure soil, water, weather, and plant health; AI and machine learning learn from this data; then the system tells farmers exactly when and how much to irrigate, fertilize, or treat plants and animals, reducing waste and improving yields.
This is like a smart billboard that learns who is likely to be interested in your product based on patterns in anonymous signals, instead of spying on people with cookies. It uses AI to guess where your best customers will be and shows ads there, without needing to track each person around the internet.
Think of this like a supercharged weather crystal ball built specifically for power markets: it predicts very detailed weather patterns that drive electricity supply and demand so traders can buy and sell power and gas at the right time and price.
This is a guide about using tools like ChatGPT-style content generators and AI media tools to create marketing content faster so more people discover and visit your brand online.
Think of this as a super-smart billboard system that doesn’t track who you are, but instead reads the page you’re on in real time and shows an ad that fits the exact topic, tone, and situation of that content.
This is like giving your marketing team a super-smart assistant that constantly studies which people click and buy, then automatically adjusts who sees your ads so you’re not wasting money showing ads to the wrong audience.
Think of this as giving your marketing team a super-smart assistant that can study what every customer is doing in real time, write tailored messages for them, decide which ad to show where, and keep learning what works so your budget isn’t wasted.
This is like having an AI assistant watch a live TV channel or livestream for you and take notes in real time—who is speaking, what’s being said, topics, scenes, and key moments—so people and systems can react instantly instead of waiting for manual review later.
This is like having a digital ad agency in a box: you type what you want to promote, and the AI helps you generate ad creatives, copy, and campaigns in minutes across channels.
Think of this as giving your marketing team a super-fast, super-smart analyst who studies every customer click, email, and ad impression, then quietly tells you: ‘show this group offer A, show that group message B, and stop wasting money on these channels.’
This is like a financial advisor for your ad budget: it looks at all your past marketing spend and results across channels (TV, search, social, email, etc.) and tells you which ones are actually working, by how much, and where to move money to get better returns.
This is like giving your online ads a motion upgrade and a built‑in coach. The system can turn static images into eye‑catching animations and automatically tell you which versions of your ads work best, so you waste less money guessing what creatives to run.
Think of the future transport system as a giant, city-wide brain. Instead of each car, bus, or train acting on its own, AI watches traffic, weather, demand, and incidents in real time and then orchestrates everything—routes, signals, pricing, and even maintenance—so people and goods move faster, safer, and cheaper.
This is like a very powerful ‘Google Maps brain’ that can look at extremely detailed satellite and aerial images, understand what’s on the ground (roads, buildings, ships, fields, etc.), and connect that with other types of data, so many different applications can reuse the same core model instead of building their own from scratch.
Think of this as a super-smart ad trader that watches billions of people’s clicks in real time and automatically decides which ad to show, to whom, at what price, and on which platform to get the best return—far faster and more accurately than any human team could.
Think of this as a guide to how modern AI can act like a very fast, tireless financial analyst: reading huge volumes of data, spotting patterns in markets or risk, and then suggesting what to do next.
This is like having a very fast junior developer who writes code for you, but this guide teaches you how to double‑check that junior’s work so it’s safe, correct, and secure before it goes into your product.
Think of your company’s security center as an airport control tower. Traditional tools watch planes (devices, users, emails). This use of AI threat hunting in Defender XDR adds new radar that also watches the AI copilots and automations you’ve deployed—so if someone hijacks your AI assistant or uses it to sneak in malware, security can see and stop it.
Think of your company’s network as a city. AI gives both the police and the criminals super-powered binoculars and autopilot cars. Defenders use AI to spot unusual behavior and block attacks faster than humans can. Hackers use AI to scan for weak doors, write convincing scam messages, and automate break‑ins at scale.
Think of this as turning today’s security analysts into ‘AI-augmented guardians’: people who use smart tools that can spot cyberattacks much faster than humans, while also learning how to control and question those tools so they don’t make dangerous mistakes.
Think of AppForge as a driving test for AI coders. It gives GPT-style models real, end‑to‑end software projects (not just toy coding questions) and checks whether they can go from an English request to a working app without a human holding their hand.
This is about the next generation of digital ad buying, where software agents act like tireless junior media buyers. Instead of humans manually tweaking bids, budgets, and targeting rules in programmatic platforms, AI agents continuously watch performance and automatically adjust campaigns to hit goals like ROAS or CPA.
Think of this as putting a smart assistant behind every part of a trip: it helps people discover where to go, picks good flights and hotels for their budget, updates prices in real time, and steps in when something goes wrong (like delays or overbooking). It learns from thousands of past trips so each new traveler gets a smoother, more personalized journey.
This is like giving every hotel guest their own 24/7 digital concierge on their phone. Guests can message a smart assistant to ask questions, request services, or get recommendations—without calling the front desk.
This is like a smart, medical-focused chatbot that explains how AI is being used in healthcare and helps people explore use cases, ideas, and benefits of AI in medicine.
This is about using smart software that learns from patterns in network traffic and user behavior to spot hackers and suspicious activity much faster than human teams or rule-based tools can, and then automatically block or contain threats before they spread.
Think of AIOps platforms as a 24/7 AI control tower for your IT systems. They watch logs, metrics, and alerts from all your tools, spot patterns humans would miss, and automatically fix or route problems before they become outages.
Think of this as a smarter, more polite billboard system for the internet. Instead of shouting the same message at everyone, AI helps show the right ad to the right person at the right time—while staying within new privacy rules.
This is like an AI pair-programmer built directly into Visual Studio Code. As you type, it suggests whole lines or blocks of code, helps write tests, explains code, and can transform comments or natural language into working code snippets.
This is like giving your developers a smart co-pilot inside JetBrains IDEs that can read and write code, explain it, and help with everyday tasks without leaving their usual tools.
Think of AI code assistants as smart copilots for programmers. As you type, they guess what you’re trying to build and suggest code, explain errors, write tests, and help you understand unfamiliar code — like an always‑available senior engineer sitting next to every developer.