A partnership between Cubic Transportation Systems (CTS) and McMaster University in Hamilton, Ontario, is exploring new technologies around traffic prediction and a heightened understanding of vulnerable road users. Cubic, a transportation and transit technology provider, has entered into a five-year partnership with the university to further develop technology products that include artificial intelligence and machine learning that takes into consideration the wide variety of users of transportation systems. "At the center of everything we do is DEI -- diversity, equity and inclusion," said Ali Emadi, professor and research chair at McMaster University. "That's what makes us different from a lot of artificial intelligence centers in academia or in industry," he added. The project, known as the Centre of Excellence for Artificial Intelligence and Smart Mobility, is also exploring the notion of "human-centered design," and populating design teams with researchers and technicians from across multiple backgrounds and expertise.

The promise is there, but AI's limitations are also coming increasingly to the fore, potentially undermining trust and leaving some questioning the real value of the technology. OECD Business and Finance Outlook 2021: AI in Business and Finance says AI has the potential to provide insights, recommendations and efficiencies for sectors ranging from health care to agriculture, but it also can create new risks or reinforce existing risks, including eroding competition, privacy and the right to fair, equal treatment. It's already'transforming' financial services, with financial markets spending up large in 2020 with global spend of more than US$50 billion in AI. But the OECD report says risks include entrenching bias, lack of explainability of financial decisions, introducing new forms of cyber attacks and automating jobs ahead of society adjusting to the changes. "Fewer than two out of five of companies that had made an investment in AI reported business gains."

The results of this year's McKinsey global survey, The state of AI in 2020, suggests that organisations are using artificial intelligence (AI) as a tool for generating value. This year's survey found that a small contingent of respondents coming from a variety of industries attribute 20% or more of their organisations' earnings before interest and taxes (EBIT) to AI. McKinsey reported that these companies plan to invest even more in AI in response to the coronavirus pandemic, which suggests that there will be a wider gap between organisations leading in the deployment of AI and the majority of companies that are struggling to capitalise on the technology. The survey found that the largest shares of respondents report revenue increases for inventory and parts optimisation, pricing and promotion, customer-service analytics, and sales and demand forecasting. According to McKinsey, the areas of AI deployment that most commonly led to cost decreases are optimisation of talent management, contact centre automation and warehouse automation. Over half of respondents who deployed AI in these areas said they had reduced costs.

Is artificial intelligence ready to move out of the labs and proofs of concept and into the mainstreams of enterprises? Leading industry observers say yes, it's ready -- but organizations may still need some preparation. "We've moved beyond the phase of'Is AI a shiny object?' toward broader mainstream adoption and actual value creation," says Bryce Hall, associate partner with McKinsey, noting that data from a survey of 2,300 executives shows a nearly 25% year-over-year increase in the use of AI in standard business processes. Hall joined McKinsey colleague Michael Chui in a recent podcast Q&A, in which they note that while AI is being widely adopted, much of the work is confined to specific narrow use cases, versus more strategic enterprise-centric adoption. "We're very early in this trend," says Chui. "As much as we're seeing this growth in adoption, less than a third of the companies that we surveyed have deployed AI in multiple businesses or functions."

AI works great in the labs, but is it ready for prime time? Is artificial intelligence ready to move out of the labs and proofs of concept and into the mainstreams of enterprises? Leading industry observers say yes, it's ready -- but organizations may still need some preparation. "We've moved beyond the phase of'Is AI a shiny object?' toward broader mainstream adoption and actual value creation," says Bryce Hall, associate partner with McKinsey, noting that data from a survey of 2,300 executives shows a nearly 25% year-over-year increase in the use of AI in standard business processes. Hall joined McKinsey colleague Michael Chui in a recent podcast Q&A, in which they note that while AI is being widely adopted, much of the work is confined to specific narrow use cases, versus more strategic enterprise-centric adoption.

AI works great in the labs, but is it ready for prime time? Is artificial intelligence ready to move out of the labs and proofs of concept and into the mainstreams of enterprises? Leading industry observers say yes, it's ready -- but organizations may still need some preparation. "We've moved beyond the phase of'Is AI a shiny object?' toward broader mainstream adoption and actual value creation," says Bryce Hall, associate partner with McKinsey, noting that data from a survey of 2,300 executives shows a nearly 25% year-over-year increase in the use of AI in standard business processes. Hall joined McKinsey colleague Michael Chui in a recent podcast Q&A, in which they note that while AI is being widely adopted, much of the work is confined to specific narrow use cases, versus more strategic enterprise-centric adoption.

Hardly a day goes by without a prediction about how artificial intelligence will radically change our lives -- driving our cars, running errands, sucking up jobs. But what is the state of enterprise AI? This complimentary document comprehensively details the elements of a strategic IT plan that are common across the board – from identifying technology gaps and risks to allocating IT resources and capabilities. You forgot to provide an Email Address. This email address doesn't appear to be valid.

Give us your feedback Thank you for your feedback. Deep learning may be one of the most overhyped of modern technologies, but there is a good chance that it will one day become the secret sauce in many different business processes. For anyone entering the workforce now -- or thinking about how to position their career for the long term -- this would be a very good time to understand its implications better. The term "deep learning" refers to the use of artificial neural networks to carry out a form of advanced pattern recognition. Algorithms are trained on large amounts of data, then applied to fresh data that is to be analysed.

BOSTON – Robots can't yet bake a souffle or fold a burrito, but they can cook up vegetables and grains and spout them into a bowl -- and are doing just that at a new fast casual restaurant in Boston. Seven autonomously swirling cooking pots -- what the restaurant calls a "never-before-seen robotic kitchen" -- hum behind the counter at Spyce, which opened Thursday in the city's downtown. Push a touch-screen menu to purchase a $7.50 meal called "Hearth." A blend of Brussels sprouts, quinoa, kale and sweet potatoes tumbles from hoppers and into one of the pots. The pot heats the food using magnetic induction, then tips to dunk the cooked meal into a bowl.

Robots can't yet bake a souffle or fold a burrito, but they can cook up vegetables and grains and spout them into a bowl -- and are doing just that at a new fast casual restaurant in Boston. Seven autonomously swirling cooking pots -- what the restaurant calls a "never-before-seen robotic kitchen" -- hum behind the counter at Spyce, which opened Thursday in the city's downtown. Push a touch-screen menu to purchase a $7.50 meal called "Hearth." A blend of Brussels sprouts, quinoa, kale and sweet potatoes tumbles from hoppers and into one of the pots. The pot heats the food using magnetic induction, then tips to dunk the cooked meal into a bowl.