One in five enterprises sees modernizing its data infrastructure for AI as the top AI initiative to ... [ ] gain a competitive edge today. These and many other fascinating insights are from Deloitte Insights' Survey: State of AI in the Enterprise, Third Edition: Thriving in the Era of Pervasive AI. A PDF of the study is available for download here (28 pp., PDF, no opt-in). Deloitte surveyed 2,737 IT and line-of-business executives between October and December 2019 from nine countries. To qualify for the study, all responding executives needed to have experience evaluating, budgeting for and implementing AI investments.

The timely deployment of smart machines is an equally critical success factor. It must coincide with the imminent upgrade of infrastructure that is enabling the digital transformation. Given that the rollout of 5G will necessitate a large-scale system overhaul, the best time to reimagine more sustainable features in your business processes or your architecture would have to be when your enterprise is investing in the upgradation. In short, it kills two birds with one stone. For example, smart ambulances are now fitted with state-of-the-art equipment to serve remote geographies in the healthcare industry.

Tidying up a household environment using a mobile manipulator poses various challenges in robotics, such as adaptation to large real-world environmental variations, and safe and robust deployment in the presence of humans.The Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global competition held in September 2021, benchmarked tidying tasks in the real home environments, and importantly, tested for full system performances.For this challenge, we developed an entire household service robot system, which leverages a data-driven approach to adapt to numerous edge cases that occur during the execution, instead of classical manual pre-programmed solutions. In this paper, we describe the core ingredients of the proposed robot system, including visual recognition, object manipulation, and motion planning. Our robot system won the second prize, verifying the effectiveness and potential of data-driven robot systems for mobile manipulation in home environments.

Artificial intelligence (AI), where machines can effectively mimic the cognitive function of the human mind, is the driving force behind machine and deep-learning technology. Simply put, AI is a set of digital tools that make machines smarter, allowing them to perform certain automated activities that would normally require human intelligence.

AI can help IoT-connected systems better analyze data, make decisions and change behaviors. Using AI is how IoT can finally deliver on its promise of a sky-high ROI. The holy grail for IoT has always been better decision-making and data-driven actions. In this quest, IoT has evolved from pure remote control to a cutting-edge technology that incorporates machine learning. IoT has always been about getting data, and then acting on the data.

Machine learning used with IoT can save both money and human effort in many use cases, such as automatically changing a thermostat based on the fluctuating cost …

Artificial intelligence tools that seek to address complex public problems need to be developed with the input of the decision-makers who will implement them, Mitzi Bolton writes. Turn on the news, social media, or catch up with friends and you'll no doubt find conversations turning to the problems of society, things that could or should be done differently. Things the government ought to fix but seems unable to address. There's the risk of exceeding planetary boundaries, increased incidence of zoonotic diseases, climate change and its related impacts, continuing debates over management of the Murray Darling Basin, disincentives to workforce participation, handling of hotel quarantine, and many more. These and other highly challenging problems, often considered in isolation, come together when global progress on the Sustainable Development Goals (SDGs) is considered.

Bottom Line: The most urgent talent management issue every business is facing today is how to improve Diversity and Inclusion (DI) by reducing the potential of bias and evaluating candidates on capabilities first. Organizations need to focus more on using AI and machine learning techniques to identify, recruit, and hire candidates based on their capabilities while removing as many potential bias triggers as possible. Businesses that are making DI an integral part of their companies are experiencing an 83% improvement in their ability to innovate, a 42% increase in team collaboration effectiveness, and a 31% improvement in customer responsiveness, according to Deloitte. A study by the American Sociological Association found that companies with the highest levels of racial diversity attain 15 times the sales revenues of those organizations with the lowest levels. McKinsey found that excelling at DI is directly related to higher profitability and value creation.

The trucking industry, and the logistics that keep it running, have become fundamental to the success of supply chains, both nationally and internationally. Without those supply chains running smoothly, allowing for just-in-time orders and millions of successful daily deliveries, industries of all types would grind to a halt. The Brexit situation, with Great Britain withdrawing from the European Union, is a perfect example of the kind of chaos a broken supply chain can create. Issues such as Brexit may have highlighted how important the trucking industry is, yet geo-political issues such as this are far from the only challenges currently facing freight and haulage, with market demands shifting quickly, and shipments growing accordingly. These issues have put trucking front and center in the eyes of those seeking industry disruption, and seismic changes to the way trucking works.

IDC forecasts that spending on AI technologies will grow to $97.9B in 2023, more than two and a half times the spending level of 2019. Click here to view original webpage at www.forbes.com