Conventional wisdom holds that you need large volumes of labeled training data to unlock value from powerful AI models. For the consumer internet companies where many of today's AI models originated, this hasn't been difficult to obtain. But for companies in other sectors -- such as industrial companies, manufacturers, health care organizations, and educational institutions -- curating labeled data in sufficient volume can be significantly more challenging. Over the past few years, AI practitioners and researchers have developed several techniques to significantly reduce the volume of labeled data needed to build accurate AI models. Using these approaches, it's often possible to build a good AI model with a fraction of the labeled data that might otherwise be needed.

Guðni Thorlacius Jóhannesson, the president of Iceland, visited MIT on Friday, engaging in talks with several campus leaders and professors, and touring the Media Lab. Jóhannesson visited the Institute along with a substantial delegation of officials and scholars from Iceland. They met with MIT scholars, who delivered a variety of presentations on research, design, and entrepreneurship; the Iceland delegation also had a particular interest in the inclusion of the Icelandic language in artificial intelligence-driven tools that automatically recognize, translate, and deploy speech and texts. "We are determined to make sure that Icelandic has a place in the digital age," Jóhannesson said. "AI plays a key role there."

In a new book about how technology will affect workers, MIT experts explain how AI is far from replacing humans — but still changing occupations.

Artificial intelligence has a bad rap. Facial recognition algorithms -- like those used by law enforcement agencies around the country -- encourage racism. Digital assistants, such as Siri and Alexa, make children ruder. Predictive algorithms, like those employed by Facebook, narrow our perspectives. Meanwhile, language translators, including Google Translate, are said to hinder meaningful emotional connection.

Some industry experts argue that machine learning (ML) will reverse an increasing trend toward passive investment funds. But although ML offers new tools that could help active investors outperform the indexes, it is unclear whether it will deliver a sustainable business model for active asset managers. Let's start with the positives A form of artificial intelligence, ML enables powerful algorithms to analyze large data sets in order make predictions against defined goals. Instead of precisely following instructions coded by humans, these algorithms self-adjust through a process of trial and error to produce increasingly more accurate prescriptions as more data comes in. ML is particularly adaptable to securities investing because the insights it garners can be acted on quickly and efficiently.

Companies are already making sophisticated marketing decisions with data and analytics. Will deep learning enable a leap forward -- or just marginal gains? Deep learning is delivering impressive results in AI applications. Apple's Siri, for example, translates the human voice into computer commands that allow iPhone owners to get answers to questions, send messages, and navigate their way to and from obscure locations. Automated driving enables people today to go hands-free on expressways, and it will eventually do the same on city streets.

In opening yesterday's AI and the Work of the Future Congress, MIT Professor Daniela Rus presented diverging views of how artificial intelligence will impact jobs worldwide. By automating certain menial tasks, experts think AI is poised to improve human quality of life, boost profits, and create jobs, said Rus, director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science. Rus then quoted a World Economic Forum study estimating AI could help create 133 million new jobs worldwide over the next five years. Juxtaposing this optimistic view, however, she noted a recent survey that found about two-thirds of Americans believe machines will soon rob humans of their careers. The economists, who predict greater productivity and new jobs?

AI is helping companies coordinate their workflows to achieve great efficiency and more synchronization. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Anyone who has ever worked for a large organization knows that information silos are a challenging fact of life. They're evident internally: The left hand doesn't always know what the right hand is doing, and employees who are supposed to be working in concert are out of sync. Companies that are in business together often don't have full information or a clear picture of their partnership.

AI is helping retailers customize their offerings, create personalized experiences, and make shopping more convenient. Customer centricity -- putting your customer at the center of your strategy -- has long been considered the holy grail of retail marketing. In the digital age, customer-centricity revolves around data and smart technologies like artificial intelligence (AI). With the help of AI, companies collect as much data as they can about their customers' wants, needs, and preferences, and then apply it to customize their offerings, create personalized shopping experiences, and make the purchase process simpler and more convenient. An example of new tools available for understanding customer habits is the Personality Insights service made possible by IBM's AI platform, Watson.

The technologies driving artificial intelligence are expanding exponentially, leading many technology experts and futurists to predict machines will soon be doing many of the jobs that humans do today. Some even predict humans could lose control over their future. While we agree about the seismic changes afoot, we don't believe this is the right way to think about it. Approaching the challenge this way assumes society has to be passive about how tomorrow's technologies are designed and implemented. The truth is there is no absolute law that determines the shape and consequences of innovation.