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Culture clash - the challenge of innovation through acquisition
IoT Business Index 2017: Transformation in Motion

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Will the covid-19 pandemic accelerate automation?

Less globalisation, more automation: the economic crisis induced by pandemic is likely to encourage a surge of labour-replacing technology. That will be disruptive, unequal and challenging for workers.

Staff of 2030: Future-Ready Teaching

Educators around the world are trying to modernise schooling to better prepare young people for the 21st century. Personalised and self-directed learning, social and emotional skills, and “issuesbased” learning that explores linkages between subjects are just a few of the ways that progressive educators are working to equip young people for contemporary realities. At the same time, the teaching workforce is transforming as a new generation enters the profession and today’s working cohort ascends into management or begins to shape education policy.

Ox, Bees or Elephant? Three scenarios examining the socio-economic impacts of artificial intelligence on Thailand

To support Thai policymakers in navigating this transition, the Institute of Public Policy and Development commissioned The Economist Intelligence Unit to conduct a foresight exercise that investigates how AI could affect key social and economic metrics in Thailand across three scenarios. In each of these scenarios, we have assumed that AI technology will substantially increase the use of computers and raise productivity. We focused our analysis on two critical and uncertain factors: the effectiveness of industrial policy and the extent of skills development in an AI-augmented economy.

AI and advanced analytics in AML: From rule-based controls to intelligence-led capabilities

<h2>Supporting AML with machine learning</h2>

<p>AI is a broad term covering multiple fields. For AML professionals, perhaps the most relevant subfield of AI is machine learning, which refers to the use of algorithms to continually improve a task, without the need for human intervention. Machine learning algorithms search for patterns within a given data set. Repeated recognition of patterns allows an algorithm to make ever more swift and accurate predictions.</p>

Talent for innovation

Talent for innovation: Getting noticed in a global market incorporates case studies of the 34 companies selected as Technology Pioneers in biotechnology/health, energy/environmental technology, and information technology.

Putting IoT to work in business operations

There has been a step-change in how companies are using Internet of Things (IoT) technology to assist with internal operations since 2017, The Economist Intelligence Unit’s reveals.

Securing IoT

The Economist Intelligence Unit’s Internet of Things (IoT) Business Index 2020, sponsored by Arm, reveals that IoT adoption has progressed significantly since 2017, both in companies’ internal operations and in their products and services. 

But this progress has come in spite of lingering security concerns, both from consumers and companies themselves. Evidently, companies that wish to pursue IoT strategies must reassure both internal and external stakeholders that they can do so securely. 

Stuck in neutral

Cars and other vehicles are the ideal example of “unconstrained” Internet of Things (IoT) devices: plenty of power and space for high-bandwidth communications and processing. That capability is, in theory at least, a platform for digital services that could enhance the driving experience and boost automakers’ coffers.

When IoT meets AI

The business value of IoT data increases with the insights that can be extracted from it. Artificial intelligence (AI) is helping many businesses extract insight from IoT data; indeed, 26% of executives survey for the Economist Intelligence Unit’s IoT Business Index 2020 say IoT data are ‘pivotal’ to their AI initiatives. 

No compromising on security

Online fraud in the business world is growing more sophisticated—and expensive

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