Rethinking the artificial intelligence race

Artificial intelligence (AI) has become a buzzword in technology in both civilian and military contexts. With interest comes a radical increase in extravagant promises, wild speculation, and over-the-top fantasies, coupled with funding to attempt to make them all possible. In spite of this fervor, AI technology must overcome several hurdles: it is costly, susceptible to data poisoning and bad design, difficult for humans to understand, and tailored for specific problems. No amount of money has eradicated these challenges, yet companies and governments have plunged headlong into developing and adopting AI wherever possible. This has bred a desire to determine who is “ahead” in the AI “race,” often by examining who is deploying or planning to deploy an AI system. But given the many problems AI faces as a technology its deployment is less of a clue about its quality and more of a snapshot of the culture and worldview of the deployer. Instead, measuring the AI race is best done by not looking at AI deployment but by taking a broader view of the underlying scientific capacity to produce it in the future.

AI Basics: The Minds We Create 

AI is both a futuristic fantasy as well as an omnipresent aspect of modern life. Artificial intelligence is a wide term that broadly encompasses anything that simulates human intelligence. It ranges from the narrow AI already present in our day-to-day lives that focuses on one specific problem (chess playing programs, email spam filters, and Roombas) to the general artificial intelligence that is the subject of science fiction (Rachel from Blade Runner, R2-D2 in Star Wars, and HAL 9000 in 2001: A Space Odyssey). Even the narrow form that we currently have and continually improve, can have significant consequences for the world by compressing time scales for decisions, automating repetitive menial tasks, sorting through large masses of data, and optimizing human behavior. The dream of general artificial intelligence has been long deferred and is likely to remain elusive if not impossible, and most progress remains with narrow AI. As early as the 1950’s researchers were conceptualizing thinking machines and developed rudimentary versions of…