#576: Disrupsjon i regnskap og finans bransjen
#555: Kunstig intelligens og forretningsutvikling
Fra Gridd.AI Robotics
#551: Anvendt AI
Jahn Thomas Fidje
Fra universitetet i Agder
#546: Fremtidens tekstanalyse gjennom AI
Per Morten Hoff
Fra Anzyz Technologies
#523 – Vi gjør AI tilgjengelig for alle.
Odd Jostein Svendsli
Fra AIA Science
#531 – Big AI vs. “hverdags-AI”
Fra Sopra Steria
#518 – Hvordan og hvorfor vi må åpne black boxen
Odd Are Svensen
#489 – Fremtidens journalistikk
Fra Fonn group
#421 – AI and transformation of the workplace
Jarle Moss Hildrum
Fra Telenor Research
#414 – AI in Telenor
Fra Telenor ASA
#384 – Kunstig intelligens for å gjøre gode fagfolk enda bedre
#339 – Future of human-machine interaction
#319 – Machine Bias
Fra Making Waves
#318 – Hva er NLP?
#311 – Virtuell assistent
Lars Ropeid Selsås
#309 – Livsviktig AI
Helge J. Bjorland
Fra Globus AI
#290: IFE og Applied AI
#215 – Ansvarlig AI
Cathrine Pihl Lyngstad
#214 – Chatbots
#200 – AI -Keiserens kunstige klær?
Fra Norsk Regnesentral
#201 – Intelligent og automatisk bildeanalyse
Fra Norsk Regnesentral
#157 – AI in Theory and Practice
Ole Jakob Mengshoel
Fra Dept. of Computer Science, NTNU
#148 – Maskinlæring for lungelyder
#114 – Kan AI erstatte Google?
#112 – Teknologien og oss
Fra Sykehjemsetaten, Oslo kommune
#68 – AI og livslang læring
#67 – Kunstig intelligens i norsk næringsliv
Anne Marthine Rustad
#65 – Global race for AI excellence
#66 – AI reduserer matsvinn
#64 – Høydimensjonale data
Fra Simula Metropolitan Center for Digital Engineering
#62 – Assisting Intelligence
Knut O Hellan
#63 – Kreativ bruk av data
#60 – De største fremskrittene i AI
#58 – Kunstig intelligens -den usynlige revolusjonen
Per Kristian Bjørkeng
#59 – Kan AI gjøre reiseopplevelsen bedre?
Fra AISPOT AS
#57 – AI kan gjøre helsetilbudet bedre
Fra Diffia AS
#55 – Overfitting vs. Personalisering
Jon Espen Ingvaldsen
#56 – Kunstig intelligens — mer kunstig enn intelligent
Fra Turning Data Into Products AS / Quantifio
#53 – Hva er greia med Big Data og AI?
Fra Universitetet i Agder
In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. More in detail, Kaplan and Haenlein define AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation”. Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.
The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring “intelligence” are often removed from the definition, a phenomenon known as the AI effect, leading to the quip in Tesler’s Theorem, “AI is whatever hasn’t been done yet.” For instance, optical character recognition is frequently excluded from “artificial intelligence”, having become a routine technology. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomously operating cars, and intelligent routing in content delivery networks and military simulations.
Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an “AI winter”), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into subfields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals (e.g. “robotics” or “machine learning”), the use of particular tools (“logic” or artificial neural networks), or deep philosophical differences. Subfields have also been based on social factors (particular institutions or the work of particular researchers).
The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field’s long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many others.
The field was founded on the claim that human intelligence “can be so precisely described that a machine can be made to simulate it”. This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by myth, fiction and philosophy since antiquity. Some people also consider AI to be a danger to humanity if it progresses unabated. Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.