General Electric Company (GE) is a global industrial technology company focused on aerospace, power, and renewable energy. Following a multi‑year restructuring, GE is transitioning into separate public companies, with GE Aerospace and GE Vernova as its primary businesses. The company develops advanced hardware and software solutions that power aircraft, energy infrastructure, and industrial systems worldwide.
Think of AlphaFold 2 as a revolutionary microscope that predicts how single proteins fold in 3D. The “next frontier” the article discusses is like upgrading from looking at a single Lego brick to understanding whole Lego machines: how multiple proteins, RNAs, DNA, and small molecules interact, move, and change shape in real time inside a cell.
This is about using AI as an ultra-fast research assistant that reads mountains of scientific data, suggests promising drug ideas, and helps scientists decide what to test next, so the slow, trial‑and‑error parts of drug discovery move much faster.
Think of this like a super-smart safety inspector for new medicines. Instead of testing every drug only in animals or long, expensive lab studies, a machine learning system studies lots of past data about how drugs affect human cells and then predicts which new drug candidates are likely to be toxic to people—before they ever reach clinical trials.
It’s like giving every truck in a fleet a smart fitness tracker. Instead of guessing risk from age or ZIP code, the insurer watches how each truck is actually driven—speeding, hard brakes, routes, and mileage—and then prices insurance based on real behavior, not rough averages.
Think of every patient as a unique garden: their genes are the soil, epigenetics is how the soil has been treated over time (fertilizer, pollution, stress), and the microbiome is the mix of plants and microbes living there. This work is about using data and models to understand how all three together affect health and how people respond to medicines, so treatments can be tailored to each person’s “garden” instead of using one-size-fits-all drugs.
This is like giving every hotel guest their own smart concierge who talks, remembers their preferences, and coordinates with hotel systems from check‑in to check‑out – but it’s software, not a person.
This is like having an always-on digital analyst that reads every customer review, support ticket, social media post, and survey response, then tells you in plain language whether people are happy or unhappy and why.
This is like giving your company a super-listening ear that reads all customer comments, reviews, and survey answers and tells you, in plain language, how people feel and why they’re happy or upset.
Imagine every product in your store has a smart price tag that quietly watches how many people want it, what competitors are charging, and how much stock you have left—and then changes the price in real time to get the best mix of profit and sales, not just on Black Friday but all season long.
Think of this as a smart control tower for a call center. It watches millions of customer interactions, spots what’s working and what’s broken, and then uses AI to help agents answer faster, better, and with less effort even when call volumes spike.
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.
This is like having a super-smart mailroom clerk for your support team who instantly reads every incoming customer request, understands what it’s about, how urgent it is, and then sends it to exactly the right person or team to handle it best.
This is like giving your customers a smart digital receptionist that can answer questions, solve common issues, and guide them 24/7 without needing a human agent on the line for every request.
This is like giving every customer service agent (and your IVR/chatbot) a super-smart digital co-pilot that can instantly read knowledge bases, past tickets, and policies to answer customers in natural language across phone, chat, and other channels.
This is like giving your customers and employees a smart, always-on digital concierge that can understand questions in normal language, look up the right information across your systems, and respond instantly on chat, voice, or other channels.
Think of Robin AI as a very fast, tireless junior lawyer that reads contracts, flags issues, and suggests edits so your human lawyers only have to make the final calls instead of doing all the manual line‑by‑line work.
Think of AutoCompose as a smart autocomplete for customer service agents: while they’re typing replies to customers, it suggests full, high‑quality responses so they mostly click, tweak, and send instead of writing from scratch.
This is like having a tireless digital scout that constantly checks competitors’ prices across the internet, compares them to yours, and suggests how you should price your products to stay competitive and profitable.
This is like a smart mechanic for power-plant valve actuators: it watches sensor data, predicts when parts are likely to fail, and also explains in plain engineering terms why it thinks a failure is coming (e.g., which pressures, temperatures, or vibrations are driving the risk).
This is like having a tireless junior lawyer who scans every contract you receive, flags risky clauses, and compares them to your standard playbook before it ever reaches your legal team.
This is like a smart assistant that reads a patient’s electronic medical record and quietly taps the doctor on the shoulder to say, “Based on all this history and lab data, this patient looks like they’re at high risk for X in the next few hours—here’s why and what to watch out for.”
Think of Zendrive as a smart driving coach that quietly rides along in your phone, watching how you drive and turning that behavior into a safety score insurers can use to price policies and prevent accidents.
This is like putting a super-smart, always-on driving coach in every car that silently watches how people drive—speeding, harsh braking, phone use—and turns that into a simple safety score insurers and fleets can use.
Think of this as a smart audit assistant for insurance claims that automatically checks what’s being claimed against what should realistically be there, flags suspicious items, and speeds up payouts for genuine claims.
This is like giving the power grid a very smart weather forecast, but instead of predicting rain, it predicts how much electricity people will use so green energy sources can be used more efficiently.
This is like an autopilot for hotel pricing and room inventory. It watches booking patterns, competitor prices, and events in the area, then automatically suggests or updates room rates and restrictions to maximize revenue each day.
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.
This is about using smart algorithms to decide who should get a loan, how much, and at what interest rate—by looking at far more data than a human could and doing it in seconds instead of days.
This is like a central control tower for banks and crypto businesses that checks who their customers are, watches their transactions for suspicious activity, and makes sure they follow all the rules in both traditional finance and crypto — all from one unified system.
Think of a pile of student essays. Instead of teachers grading every essay one by one with a long rubric, the system just keeps asking: ‘Which of these two is better?’ After lots of these quick comparisons, the software works out a reliable score for every piece of work. It’s like ranking players in a tournament, but for writing and exams.
This is like putting a smart black box and lie detector in the car for insurers. The telematics device and apps track how, when, and where a car is driven, then AI looks for driving and claims patterns that don’t add up—flagging suspicious cases for human investigators before money is paid out.
Think of this as a smart, always-on receptionist and helpdesk team that can talk to customers by chat, voice, or video, answer most questions instantly, route complex issues to humans, and learn from every interaction to get better over time.
This is like an air-traffic control tower for hospitals that uses AI to watch every bed, patient movement, and bottleneck in real time, then recommends what to do next so patients don’t sit waiting in hallways or ERs.
Think of this as a smart black box in your customer’s phone that can instantly sense a crash, estimate how bad it is, and trigger the right insurance response without the driver needing to call in and explain what happened.
This is like giving a sugarcane farm a smart “health scanner” from the sky. Satellites, drones, and sensors constantly watch the fields and an AI system turns those images and readings into simple, field-level advice: which parts of the farm are thirsty, which are suffering from salty soils, and where plants need more or less nitrogen fertilizer.
Think of this as turning the power grid into a ‘smart internet of energy’ where 5G connects all the equipment and AI acts like a traffic controller, constantly balancing where electricity should go, when to store it, and how to avoid waste or outages.
This is like giving ER triage nurses a smart calculator that looks at a patient’s vital signs and symptoms and helps decide how urgent their case is, so the sickest people are seen first and fewer patients are mis-prioritized.
This is like a smart early‑warning system for telecom companies that watches customer behavior and complaints, predicts who is likely to cancel soon, and tells your team exactly which customers to contact and what offers or actions will keep them from leaving.
Think of this as a digital mechanic that constantly listens to your vehicles, trains, or equipment, predicts when something is about to break, and tells you exactly when to bring it in for service so you avoid breakdowns and warranty fights.
Think of Genvid as a studio that mixes video games, live shows, and AI into one big interactive stream, where viewers don’t just watch but can influence what happens in real time—often powered behind the scenes by AI-generated content and logic.
Think of this as a smart nervous system for vehicles and mobile assets: sensors and GPS on trucks, trailers, and equipment continuously send data to an AI "brain" that helps dispatchers, safety teams, and operations people run fleets more safely and efficiently.
Think of this as a playbook of ways to use AI as the ‘brains’ of a modern factory—helping machines predict failures, optimize production lines, and improve quality with less human guesswork.
This is like giving radiologists a super-smart assistant that looks at heart MRI scans and automatically measures how well the heart is working, then flags patterns that match different heart diseases—much faster and sometimes more consistently than a human reading every image by hand.
Think of this as a super-smart price-watching assistant that constantly scans your competitors’ online prices and product assortments, then tells you how to adjust your own prices to stay competitive and profitable—without a human staring at spreadsheets all day.
This is like giving every call center and support agent a super-smart digital co-worker that can understand customer issues, look things up across systems, and take actions (like updating an order or issuing a refund) instead of just suggesting responses.
Think of a telecom network as a city’s road system. Today, every new business idea (self-driving cars, smart factories, telemedicine) needs new “lanes” and “traffic rules.” AI-enabled network transformation is like upgrading the city with smart, self-managing roads that automatically open new lanes, reroute traffic, and prioritize ambulances over commuters. This lets telecom operators quickly create and sell new digital services without rebuilding the whole road system each time.
Think of AI in oil and gas as a super-smart control room operator that never sleeps. It constantly watches wells, pipes, and equipment data, predicts when something will break, and suggests how to squeeze more oil and gas out of the ground at lower cost and risk.
Think of this as a smart co‑pilot for radiology departments: it sits on top of imaging systems, helps route and prioritize scans, spots patterns, and surfaces the right information so radiologists and hospitals can move faster and make fewer mistakes.
Think of this as a smart scheduling assistant for hospital operating rooms that learns from past data and live conditions (staffing, emergencies, cancellations) to constantly reshuffle the theatre list so more patients get treated on time with fewer last‑minute surprises.
Think of this as a hedge fund where thousands of super-fast robot analysts scan markets, news, and data 24/7, then automatically place trades based on patterns they’ve learned instead of human hunches.
This is like giving the power company a very smart weather forecast, but instead of predicting rain or sunshine, it predicts how much electricity people will use in the next few hours or days using machine learning.
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 giving a smart calculator to your pricing team that constantly watches the market, your competitors, and customer reactions, then recommends better prices that boost profit without breaking customer trust.
Think of this as a data “brain” for sports leagues, broadcasters, and betting operators that watches every game, learns what fans enjoy, and then helps serve them the right highlights, stats, and betting offers at the right moment on the right screen.
Think of this as putting a super-fast robot scientist and a tireless data analyst together in your lab. The robot runs thousands of chemistry and biology experiments automatically, while the AI watches the data, spots patterns humans would miss, and suggests the next best experiments to run to find promising new drugs much sooner.
This is like using extremely smart microscopes and calculators on a computer to design new medicines before you ever mix chemicals in a lab. The software predicts which molecules are most likely to work, so scientists test 100 promising ideas instead of 10,000 random ones.
This is like a real-time control room for sports and esports fans: it listens to what fans do and say across channels, then tells teams, leagues, and brands who their fans are, what they care about, and how to keep them engaged and buying.
This is like giving ER doctors a super-fast, data-driven second opinion that watches the patient’s information in real time and quietly flags risks or suggests next steps, without replacing the doctor’s judgment.
Think of this as giving the emergency department a very fast, very experienced digital assistant that helps doctors and nurses notice critical problems sooner, choose better tests and treatments, and move patients through the system more efficiently — especially when things are chaotic and time-sensitive.
This is like giving an AI a chest X-ray or MRI scan and having it write the first draft of the radiologist’s report, instead of the doctor starting from a blank page. The doctor still reviews and edits, but the AI does the heavy lifting of describing what it sees.
This is like a smart farming advisor that looks at past harvests, weather, and soil data to suggest which crop to plant on a field and how much yield to expect, instead of farmers relying only on experience and guesswork.
This is like giving your claims department a tireless digital assistant that reads claim forms, photos, and documents, checks them against policy rules and past cases, and then drafts decisions and payouts for humans to approve—rather than people doing everything manually.
Think of AI in drug discovery as a super-fast, never-tired lab assistant that can read millions of scientific papers, simulate how molecules behave in the body, and shortlist the most promising drug candidates long before a human team could finish the first pass.
Think of AI as a super-fast, tireless scientist that can read every paper ever written, simulate thousands of experiments in a day, and flag the most promising drug ideas long before humans could. Instead of running blind, drug companies use AI as a GPS that suggests the best routes, warns about dead ends, and helps them reach new medicines faster and cheaper.
Think of this as a smart co‑pilot for nurses: it watches patient data, compares it to what’s happened with thousands of similar patients before, and then suggests what to watch out for and what actions might be needed—while the nurse stays in full control.
Think of it as a “check engine” light on steroids for jets, ships, and vehicles: AI constantly watches sensor data and maintenance logs and warns commanders *before* something breaks, so they can fix it during downtime instead of in the middle of a mission.
This is like giving every shopper their own smart salesperson who knows what they like and automatically suggests the right products over SMS, WhatsApp, or other channels powered by Plivo.
This is like building a team of intelligent, robotic guard dogs and watchtowers for the military and national security forces, combining American software brains with UAE’s defense hardware and regional access. The joint venture designs and builds autonomous drones, towers, and command software that can watch, patrol, and react with minimal human input.
This is like giving air battle commanders a super-fast, tireless digital staff officer that watches all the radar screens, sensor feeds, and intelligence reports at once, then suggests the best options in seconds instead of minutes.
This is like a very smart mechanic for jet engines that continuously listens to many different sensors and, using patterns learned from past engines, estimates how much life is left before something needs repair or replacement.
This is about teaching factories to "take care of themselves." Machines learn to warn you before they break, adjust their own settings for quality and efficiency, and eventually coordinate with each other so the whole plant runs with less human babysitting and fewer surprises.
This is like giving scientists an AI-powered CAD tool for proteins: instead of slowly guessing and checking what shape a protein will fold into or how to tweak it, the AI can rapidly predict structures and suggest new protein designs on a computer before they’re ever made in a lab.
This is like having a super-fast digital investigator that reviews every insurance claim, compares it against millions of past cases, and highlights which ones look suspicious so your human fraud team can focus where it matters most.
This is like a smart autopilot for renewable power plants that mixes solar, wind, and batteries. It predicts how much energy you’ll get from the sun and wind, how much your customers will use, and then automatically decides when to store, sell, or buy electricity to save money and keep the lights on.
This is a step‑by‑step playbook for building a robot‑driven investment fund. Instead of human stock pickers, you design and deploy computer programs that systematically search for patterns in market data and trade automatically.
This is like a hyper-fast, giant interactive map and dashboard that lets insurers watch how thousands or millions of cars are being driven—speeding, hard braking, where and when they drive—so they can price policies more fairly and spot risks in near real time.
This is like giving your power plant or energy equipment a “check engine” light that warns you days or weeks before something breaks, instead of after it fails. Sensors continually watch vibration, temperature, pressure, etc., and machine‑learning models learn the normal patterns so they can flag early signs of trouble.
This is like a smart mechanic for fleets and industrial equipment. It constantly “listens” to machines, spots early warning signs of failure, and tells you when to service them before they break down.
This is like giving a superpowered microscope and a pattern-spotting robot to a drug lab. The system runs huge numbers of biological experiments, turns the images and data into a “map” of how cells react, and then uses AI to quickly suggest which molecules could become medicines, instead of scientists guessing and testing one-by-one over many years.
This is like giving the power grid a smart navigation system that can instantly reroute electricity around traffic jams and accidents so the lights stay on and the roads (power lines) don’t get overloaded or damaged.
Think of this as an AI-powered security guard for banks and fintechs that continuously watches every transaction, compares it against patterns of fraud and money laundering, and flags only the truly risky ones so compliance teams don’t drown in false alarms.
This is a forward-looking report about how banks and financial institutions will use smarter AI “radar systems” to spot criminals and suspicious transactions before they cause damage, instead of just ticking boxes for compliance after the fact.
Think of this as a radar system for banks that tries to spot criminals and suspicious money flows before they hit the shore, instead of just filling out paperwork after the storm has already passed. It uses AI and advanced analytics to turn huge amounts of data into early-warning signals for financial crime teams.
It’s like an autopilot for your room rates: the system constantly watches demand, competitors, events, and booking patterns, then adjusts prices in real time to sell the right room to the right guest at the best possible price.
Think of this as a super-smart referee for your mobile ads. It watches every tap, install, and purchase across apps and channels, then decides which ad truly deserves the credit—while trying not to expose or misuse people’s personal data.
This is like plugging a smart insurance mini‑app directly into other companies’ websites and apps. When a customer buys a flight, a gadget, or books a trip, Chubb’s AI quietly figures out the right kind of insurance, price, and wording for that specific customer and moment, and offers it in one click without sending them to a separate insurance site.
Think of this as a smart mechanic that lives inside your machines. It constantly listens and watches for early signs of trouble, tells you what is likely to break and when, and even recommends the best time and way to fix it so you avoid unplanned downtime.
This is like having a smart early‑warning radar on your customer calls. It quietly watches patterns in how often people call, what they call about, and how their tone changes, then flags who is most likely to leave so your team can step in before they cancel.
This is like giving radiology departments a smart co-pilot: AI that continuously watches imaging workflows, flags inefficiencies or risks, suggests protocol improvements, and can even pre-analyze images—so radiologists and techs can focus on complex cases rather than routine grunt work.
This is like hiring a smart mechanic that constantly listens to all your vehicles and equipment, predicts what’s about to break, and schedules repairs before anything actually fails or delays service.
This is like giving a car or engine a brain that learns to “listen” to its own sensors and predict how much life it has left before something fails. Instead of engineers handcrafting dozens of rules and features, the model learns directly from raw sensor data when parts will wear out.
Like a smart in-store salesperson for your website that quietly watches what each shopper browses and buys, then suggests the most relevant products they’re likely to want next.
Imagine a smart legal assistant that reads large volumes of laws, contracts, and case documents and automatically pulls out the important facts, clauses, and legal concepts so lawyers don’t have to search manually.
Think of Luminance as a super-fast junior lawyer that can read huge piles of contracts, highlight key clauses, and answer questions about them in plain English, but always within law-firm standards for accuracy and control.
This is like giving oncologists a super-assistant that can read many different kinds of medical information at once—genomic profiles, imaging, lab results, and clinical notes—and then suggest patterns, risks, and treatment options that would be hard for any one human to spot alone.
This is like putting a fitness tracker on your car trips: it watches how safely you actually drive (speeding, hard braking, phone use) and then lets insurers price your car insurance based on real driving behavior instead of just your age, ZIP code, and credit score.
This is like giving every call center agent a super-smart sidekick that listens to customer interactions in real time, figures out what the customer is feeling and wants, and then quietly tells the agent the best next thing to say or do.
Think of this as an AI co-pilot for genetic testing labs and clinicians: it reads huge DNA files, compares them to medical and genomic knowledge, and highlights which genetic changes are likely to matter for a patient’s disease and treatment options.
This is like putting a smart ‘check engine’ light on every aircraft part and piece of ground equipment. Instead of waiting for something to break, Azure’s AI watches sensor data and tells you in advance when a component is likely to fail so you can fix it during planned downtime.
This is about using AI as an always‑on radar and autopilot for the supply chain: it constantly scans for risks (like delays, shortages, demand spikes), predicts problems before they hit, and suggests or triggers responses so the business can keep products flowing to customers.