IBM (International Business Machines Corporation) is a global technology and consulting company known for enterprise hardware, software, and services, with a strong focus on hybrid cloud and artificial intelligence. Founded in 1911 and headquartered in Armonk, New York, IBM serves large enterprises and governments worldwide. The company is a major contributor to AI research and commercialization through its IBM watsonx platform and IBM Research labs.
AI runs smart tests automatically every time developers change code, so teams get quick feedback before releasing updates.
This is like having a smart assistant watch all your videos and automatically create a searchable index of whatâs said, who appears, where logos show up, and key momentsâso teams can quickly find and reuse the right clips without manually scrubbing through footage.
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 this as an earlyâwarning radar for student success. It looks at studentsâ past grades, attendance, and other records and then predicts who is likely to do well or struggle, so teachers and administrators can step in before problems become failures.
This is like giving your company a superâlistener that reads what customers write (emails, chats, reviews, social posts) and instantly tells you if theyâre happy, angry, or confusedâat large scale and in real time.
Think of todayâs big AI models as brilliant general doctors who know a little about everything but arenât yet safe or precise enough to treat complex, highârisk patients. This paper is about how to retrain and constrain those general doctors so they can safely become topâtier specialists in specific medical tasks, like reading scans, summarizing patient records, or supporting treatment decisions.
This is like having a very smart auditor that has learned from years of historical tax returns. It scans new returns and flags the suspicious ones that donât âlook rightâ based on patterns seen in past fraud cases, so human investigators focus only on the riskiest filings.
Think of this as a specialist AI toolkit for retailers and consumer packaged goods brands that helps them better understand shoppers, predict demand, and personalize experiences across stores and ecommerceâlike having a data-driven co-pilot for merchandising, marketing, and operations.
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.
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 every sports fan a smart digital concierge that learns what they loveâseats, merch, highlights, statsâand quietly adjusts the entire game-day and at-home experience around them.
This is like giving every hotel guest their own smart concierge who talks, remembers their preferences, and coordinates with hotel systems from checkâin to checkâout â but itâs software, not a person.
This is like putting a super-fast, super-smart coach and statistician inside the live UFC broadcast. It watches the fight in real time, analyzes every strike, movement, and situation, and instantly tells fans whatâs happening and why it matters, in simple on-screen insights.
This is like an always-on AI control tower for your IT systems that watches all your apps, servers, and cloud services, spots issues before users notice, and tells your teams exactly what to fix and why.
This is about using open source AI tools as a smart control room for IT operations: the AI watches logs, metrics, and alerts from your systems, spots issues early, and can even fix some of them automaticallyâwithout needing an army of engineers staring at dashboards all day.
Think of a Security Operations Center as an airport control tower watching thousands of planes (devices, users, apps) at once. Traditional tools show you every single radar blip and alarm; humans get overwhelmed and miss real threats. AI- and ML-powered SIEM act like an assistant that learns normal flight patterns, filters out the noise, and flags only the suspicious flights that may be hijacked â and often does it in real time.
Think of this as a super-smart test analyst that reads your SAP setup, business processes, and change logs and then writes, updates, and runs SAP test cases for youâso your team mainly reviews and approves instead of building everything by hand.
This is like having an AI assistant watch a live TV channel or livestream for you and take notes in real timeâwho is speaking, whatâs being said, topics, scenes, and key momentsâso people and systems can react instantly instead of waiting for manual review later.
This is like having an early-warning radar for unhappy phone or internet customers. The AI watches usage and support patterns and raises a flag when someone looks likely to cancel, so your team can reach out before they actually leave.
This is like an earlyâwarning system for phone and internet providers: it studies past customers who left and learns patterns so it can flag which current customers are most likely to cancel soon, giving the company time to intervene with offers or service improvements.
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. Thatâs what generative AI brings to fraud prevention: it doesnât just flag âweirdâ behavior, it also helps investigate, summarize, and respond to it much faster.
Imagine giving your software tester a super-smart assistant that can read requirements, write test cases, suggest missing checks, and even help explain bugsâjust by talking to it in natural language. This paper surveys how those assistants, powered by large language models like ChatGPT, are being used in software testing and what still goes wrong.
This is about using AI as a smart assistant for hiring and HR: it helps you sift through piles of resumes, screen candidates, and manage communication so your team can focus on interviewing and making better hiring decisions.
This is like having a super-attentive auditor watch every call, text, and data charge in real time and instantly flag anything that looks suspicious, instead of waiting for a human to notice an odd bill weeks later.
Think of a modern power utility as an enormous, complex train set: thousands of tracks, switches, and trains (power plants, lines, and customers) all moving at once. AI is like a smart traffic controller that watches everything in real time, predicts where problems will happen, and automatically reroutes and reschedules to keep the system running safely, cheaply, and reliably.
Think of this as a citywide âcontrol towerâ that watches whatâs happeningâtraffic, utilities, emergency calls, citizen requestsâand then uses AI to suggest faster, cheaper, safer ways to run city services.
Imagine giving your fraud investigators a tireless digital assistant that reads billions of transactions, emails, and claims every day, flags anything that âlooks off,â and explains why itâs suspicious so humans can step in before the money is gone.
Imagine your entire IT environmentâservers, networks, apps, cloud servicesâconstantly watched by a smart assistant that never sleeps. It reads all the logs, alerts, tickets, and performance data, spots early warning signs, figures out whatâs really important, suggests fixes, and in many cases can trigger automated responses before users even notice a problem.
This is like hiring a smart mechanic that constantly listens to all your vehicles and equipment, predicts whatâs about to break, and schedules repairs before anything actually fails or delays service.
This is like giving European police a supercharged search and pattern-spotting engine that can sift through huge piles of digital informationâmessages, photos, travel records, financial dataâto flag suspicious links between people, places, and events that humans would struggle to see in time.
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 giving doctors a super-smart assistant that has read millions of medical cases and guidelines, then quietly whispers, âHere are the likely diagnoses and what to check nextâ while the doctor is still seeing the patientâespecially to catch diseases earlier than usual.
This is about using machine learning as a smart âcheck engineâ light for factories and vehicles. Instead of waiting for a part to fail or doing maintenance on a fixed calendar, models watch sensor data (vibration, temperature, voltage, etc.) and warn you ahead of time when something is likely to break so you can fix it before it causes downtime.
This is like a fraud radar and GPS for government benefit programs: it helps agencies see where grant and benefit dollars are really going, spot suspicious applications early, and target oversight where it matters most.
This is like a digital security guard that constantly watches phone and network activity, learns what ânormalâ looks like, and instantly flags suspicious patterns that might indicate fraud or security threatsâmuch faster and more accurately than human teams alone.
This is like a super-powered search engine for video: instead of scrubbing through hours of footage, you can type what youâre looking for and the AI jumps you straight to the right moments.
Think of this as a field guide to all the ways computers can learn from medical and pharma dataâlike a tireless junior doctor and data analyst rolled into oneâto help spot diseases earlier, pick better treatments, and run hospitals and clinical trials more efficiently.
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 â much faster and more accurately than human teams alone.
Think of Azure AI Video Indexer as an AI librarian for all your videos. It automatically watches every video, recognizes people, objects, brands, spoken words, and emotions, and then turns that into searchable labels and timelines so your teams can instantly find the exact moments they need instead of scrubbing through hours of footage.
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.
Think of this as putting a âsmart brainâ on top of every critical piece of oil & gas equipment. It constantly listens to sensors, learns what ânormalâ looks like, and warns you before something breaks so you can fix it at the best possible time.
This is like putting a smart âcheck-engineâ light on every critical asset in an oil & gas operation. Instead of waiting for something to break, software constantly watches sensor data and warns you in advance when a pump, compressor, or pipeline component is likely to fail, so you can fix it during planned downtime.
This is like giving your call center or helpdesk a smart ear that listens to what customers say (emails, chats, social posts) and instantly tells you if theyâre happy, angry, or worried, using prebuilt AI from cloud providers.
This is like giving a government benefits program a smart security camera for money flows: instead of waiting until money is stolen or misused and then trying to claw it back, AI watches transactions in real time and flags suspicious behavior before the money leaves the door.
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.
This is like putting a smart safety inspector in front of your companyâs AI chatbot. Before the AI answers, the inspector checks if the question or answer is unsafe (toxic, leaking secrets, nonâcompliant) and blocks or rewrites it.
Think of AIOps as an AI control tower watching all your IT systems 24/7. It reads all the logs, alerts, tickets, and metrics, spots patterns humans miss, and then either recommends or automatically takes actions to keep systems healthy and prevent outages.
Think of AIOps as an alwaysâon control tower for your IT systems that watches all the logs, alerts and performance metrics, spots issues early, and suggests or triggers fixes automaticallyâlike an experienced operations team that never sleeps and reads everything at once.
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 like an AI control tower for your IT systems that constantly watches logs, metrics, and alerts, spots issues before humans notice them, and suggests or triggers fixes automatically.
Think of this as a control tower that uses AI to watch over all your IT systems, predict issues, and help fix them automatically before they impact customers.
Think of this as turning todayâs security analysts into âAI-augmented guardiansâ: 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 donât make dangerous mistakes.
Think of it as a 24/7 security guard that watches every phone call, text, and transaction in real time and raises a flag when something looks like fraud, even if no human has seen that pattern before.
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.
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.
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.
This is like having a warning light on your customer base: it looks at past customer behavior and contracts and predicts who is likely to cancel their phone/internet service soon, so you can reach out before they leave.
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 giving fraud investigators a super-smart digital assistant that can scan huge amounts of payments, claims, and case files in real time and yell âthis looks suspiciousâ long before a human could spot the pattern.
This is like a smart mechanic for fleets and industrial equipment. It constantly âlistensâ to machines, spots early warning signs of failure, and tells you when to service them before they break down.
This is like giving your power plant or energy equipment a âcheck engineâ light that warns you days or weeks before something breaks, instead of after it fails. Sensors continually watch vibration, temperature, pressure, etc., and machineâlearning models learn the normal patterns so they can flag early signs of trouble.
Think of a bankâs AI like a super-fast junior loan officer that reviews thousands of applications a day. This paper is about putting clear rules, guardrails, and audits around that junior officer so it doesnât secretly treat some groups of customers worse than others, even by accident.
Think of a city where every bus, traffic light, and parking space can talk to each other in real time, and an AI âtraffic conductorâ continuously listens and adjusts things so people and goods move faster and more safely with less waste.
This is like a supercharged weather crystal ball built with AI, tailored for people trading electricity and gas. Instead of just saying whether it will rain, it predicts the kind of weather details that move energy prices and grid demand, faster and often more accurately than traditional forecasts.
Think of this as a âmedical weather forecastâ system powered by AI: it looks at a huge mix of patient data (labs, scans, genetics, history) to predict who is likely to get which disease and which treatment is most likely to work for each person.
Think of this as a smart co-pilot for buildings: it watches how your facilities are used, how equipment behaves, and what work orders come in, then suggests what to fix first, when to schedule maintenance, and how to run the building cheaper and smoother.
Think of AIOps on AWS as putting an autopilot on your IT operations. It watches logs, metrics, and alerts across your cloud systems 24/7, learns what ânormalâ looks like, and then automatically flags problems, finds root causes faster, and can even fix some issues without a human jumping in.
Like having a tireless medical research assistant who can instantly read thousands of PubMed papers and summarize the most relevant evidence for your drug or disease area.
This is about using smart software and robots as a âdigital brainâ for minesâhelping decide where to dig, how to run equipment, and how to keep workers safe, based on huge amounts of data from sensors, machines, and geological surveys.
This is like giving an insurance company a super-sleuth that reads every claim, spots suspicious patterns across people and companies, and raises red flags before money goes out the door.
This is like giving a car or engine a brain that learns to âlistenâ 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.
This is like giving the power grid a very smart weather and wind expert who can predict how much electricity wind farms will produce over the next minutes, hours, and days so operators can plan ahead safely and cheaply.
Think of AIOps as an always-on "control tower" for your IT systems that watches all logs, alerts, and metrics at once, spots real problems in the noise, and suggests or triggers fixes before users feel the pain.
Like a smart dating app for clinical research, this AI reads peopleâs health records and trial requirements to quickly find the best matches between volunteers and studies.
Like having a tireless medical librarian who scans thousands of scientific papers and automatically pulls out the key facts about how drugs work and what side effects they cause.
Like having a tireless medical librarian that can instantly read PubMed articles and explain what the clinical evidence says in plain language for your drug or disease area.
Like giving your drug development team a super-fast librarian that can read thousands of PubMed papers and pull out exactly the evidence you need for a medicine or indication.