Technology is a transformative force, and there is currently no technology more transformative than AI. Because AI encompasses so many different fields -- from robotics to deep learning to self-driving cars -- it can be hard to quantify the exact impact it has had on the economy and on individual industries. Whether you're heavily involved within the industry or an outside observer, by examining the different use cases and successes (or failures) that AI has been a part of, you can gain a better understanding of how to implement AI within your own organization, and how to generate the most value from it.
RIO DE JANEIRO, BRAZIL – iFood is planning to invest US$20 million in opening an AI learning center to strengthen ties with the tech industry. With an expected staff of 100 people by the end of the year, everything from machine learning, deep learning, behavioral science, and logistics will be covered. All of this is part of iFood's US$500 million funding round that began last year. São Paulo-based iFood is one of Latin America's biggest and most successful startup food delivery company. Seeing how the international food delivery ecosystem is worth around US$94 billion, it's easy to understand why iFood takes digital innovations so seriously.
A new learning system developed by MIT researchers improves robots' abilities to mold materials into target shapes and make predictions about interacting with solid objects and liquids. The system, known as a learning-based particle simulator, could give industrial robots a more refined touch -- and it may have fun applications in personal robotics, such as modelling clay shapes or rolling sticky rice for sushi. In robotic planning, physical simulators are models that capture how different materials respond to force. Robots are "trained" using the models, to predict the outcomes of their interactions with objects, such as pushing a solid box or poking deformable clay. But traditional learning-based simulators mainly focus on rigid objects and are unable to handle fluids or softer objects.
The number of robots around the world is increasing rapidly. And it's said that automation will threatening more than 800m jobs worldwide by 2030. In the UK, it's claimed robots will replace 3.6m workers by this date, which means one in five British jobs would be performed by an intelligent machine. Jobs in higher education are no exception – with recent studies showing a rapid advancement in the use of these technologies in universities. The full potential of these disruptive technologies is yet to be discovered, but their impact on teaching and learning is expected to be huge.
AI can be applied in sectors such as agriculture, health, and education, and Moustapha Cisse, the research scientist heading up Google's AI efforts in Africa, says his team's goal is to provide developers with the necessary research needed to build products that can solve problems that Africa faces today. "Most of what we do in our research centers at Google and not just in Accra, we publish it and open-source code, so that everybody can use it to build all sorts of things," he said. Cisse mentioned the app used by the Tanzanian farmer, to diagnose her cassava's disease as an example of the type of product his team plans to collaborate on with relevant institutes across various sectors. "A team of Pennsylvania University and the International Institute of Tropical Agriculture using TensorFlow to build new artificial intelligence models that are deployed on phones to diagnose crop disease. "This wasn't done by us but by people who use the tools we built.
If you haven't searched for a job in recent years, things have changed significantly and will continue to evolve thanks to artificial intelligence (AI). According to a Korn Ferry Global survey, 63% of respondents said AI had altered the way recruiting happens in their organization. Not only do candidates have to get past human gatekeepers when they are searching for a new job, but they also have to pass the screening of artificial intelligence that continues to become more sophisticated. Recruiting and hiring new employees is an expensive endeavor for organizations, so they want to do all that's possible to find candidates who will make valuable long-term employees for a good return on their recruitment investment. Here are a few things candidates and organizations need to keep in mind when AI is part of the job search.
Google is investing heavily in cloud-computing services -- services that help other businesses build and run software -- which it expects to be one of its primary economic engines in the years to come. And after snapping up such a large portion of the world's top A.I researchers, it has a means of jump-starting this engine. Neural networks are rapidly accelerating the development of A.I. Rather than building an image-recognition service or a language translation app by hand, one line of code at a time, engineers can much more quickly build an algorithm that learns tasks on its own. By analyzing the sounds in a vast collection of old technical support calls, for instance, a machine-learning algorithm can learn to recognize spoken words. But building a neural network is not like building a website or some run-of-the-mill smartphone app.
Martha serves CIOs and other tech leaders, helping them understand the impact of emerging technologies on their business. Martha provides in-depth coverage of blockchain technology and business intelligence (BI), as well as analytics and artificial intelligence at a strategic level. In her blockchain research, Martha focuses on demystifying this emerging technology, helping CIOs and strategy teams identify appropriate use cases and navigate the broad ecosystem of startups and established providers offering software and services. In her BI research, Martha analyzes the effect of emerging technologies and business pressures on the way BI capabilities are managed and delivered, and she helps CIOs and their business partners develop data and analytics strategies fit for the digital age.Martha earned an M.A. in English literature, American studies, and modern history from the University of Erlangen-Nürnberg in Germany. A qualified translator, Martha is fully bilingual in English and German.
Industry experts, competitors, and even your customers are talking about machine learning and artificial intelligence. The terms, while used widely and interchangeably, are often misunderstood and carry a narrow definition. Both machine learning and artificial intelligence have distinct and practical applications for your business – not only driverless cars! Machine learning is the process of building and training models to process data. In this capacity, your models are learning from your data to make better predictions.