The first episode of Sesame Street in 1969 included a segment called "One of These Things Is Not Like the Other." Viewers were asked to consider a poster that displayed three 2s and one W, and to decide -- while singing along to the game's eponymous jingle -- which symbol didn't belong. Dozens of episodes of Sesame Street repeated the game, comparing everything from abstract patterns to plates of vegetables. Kids never had to relearn the rules. Understanding the distinction between "same" and "different" was enough.

Science fiction is becoming reality as increasingly intelligent machines are gradually emerging -- ones that not only specialize in things like chess, but that can also carry out higher-level reasoning, or even answer deep philosophical questions. For the past few decades, experts have been collectively bending their efforts toward the creation of such a human-like artificial intelligence, or a so-called "strong" or artificial general intelligence (AGI), which can learn to perform a wide range of tasks as easily as a human might. But while current AI development may take some inspiration from the neuroscience of the human brain, is it actually appropriate to compare the way AI processes information with the way humans do it? The answer to that question depends on how experiments are set up, and how AI models are structured and trained, according to new research from a team of German researchers from the University of Tübingen and other research institutes. The team's study suggests that because of the differences between the way AI and humans arrive at such decisions, any generalizations from such a comparison may not be completely reliable, especially if machines are used to automate critical tasks.

During the 13 years he spent in banking, Tomasz Borowski's career spanned operations, risk management, and product management, where he also witnessed the brutality and unprofessional methods that conventional debt collection agencies adopted to retrieve outstanding balances. He observed the same thing when he moved to Ukraine in 2005 to continue his banking career. He did his own research on alternative methods and came to know about two digital debt collection companies--US-based TrueAccord and Polish debt collection agency Kruk SA--both worth over a billion US dollars today. "I decided to move to Southeast Asia and began exploring the market here. It was obvious to me that the situation here was similar to Ukraine. I decided to utilize my experience in finance and create a professional credit management services company to help change this market," Borowski told KrASIA.

With its announcement last week that it will purchase enterprise artificial intelligence (AI) and automation vendor Rage Frameworks, Genpact became the first IT and business process service provider to acquire an AI platform. The addition of Rage Frameworks, which has been applying machine learning and language processing to build intelligent automation platforms for financial services, capital markets and supply chains, "will take Genpact deeper into integrating semi and unstructured data and AI, where we see the true marriage of business processes with clever technology and self-developing algorithms," says Phil Fersht, CEO of outsourcing analyst and consulting firm HfS Research. On the surface, another acquisition of a niche automation vendor by an IT service provider might seem unremarkable. But industry watchers say this may be the tipping point where focus will shift from back-office automation to integrating and intelligently automating front- and middle-office functions. "It represents an evolution of the back-office oriented automation acquisitions that many outsourcing providers have made over the past several years," says David Borowski, director with outsourcing consultancy Pace Harmon.