If you just include "machine learning" in your pitch you can add a zero on to the end of your valuation. Machine Learning (ML) and the Internet of Things (IoT) have been huge buzzwords in the past few years, much of which has been hype and much of which reflects their profound potential. The above quote came somewhat jokingly from an investor, but it has some truth to it too. Given the hype around machine learning and IoT and the broad range of application to which they can be brought to bear, it can be difficult to cut through the noise and understand where the actual value lies. In this post, I'll explain how machine learning can be valuable for IoT, when it's appropriate to use, and some machine learning applications and use cases currently out in the world today.

IBM has teamed up with a Montreal-based Institute for Data Valorization (IVADO) to jointly develop artificial intelligence (AI) products, with plans to add another 100 technology professionals to its innovation center. The innovation center, launched in 2016 for providing digital transformation services, will henceforth focus on AI and Salesforce expertise, the American technology giant stated in a press release. "Research in AI is quickly expanding worldwide – and particularly in Montréal – but AI is far from having reached its full potential in delivering concrete results for businesses," said Claude Guay, General Manager, IBM Services, Canada. "IBM's CIC in Montréal will focus on delivering value to its clients through applied AI and bringing real solutions to real problems." The new recruits the company is looking for will work as machine learning engineers, data scientists, full stack developers, or data engineers.

Maura Kolb is more familiar with analyzing rock samples than computer code, but as exploration manager at Goldcorp's Red Lake gold mine, she now finds herself in charge of an innovative approach to exploration that involves both. The project, in partnership with IBM, uses IBM's Watson artificial intelligence platform to identify exploration targets at the Ontario mine. Since the project was launched in March 2017 at the Disrupt Mining event at PDAC, Goldcorp says the time it takes to process survey data has fallen by 97 per cent. "Being able to go through [our data] quickly, that's something no one else can do right now," said Kolb. "We can ask questions of our data that we haven't been able to in the past." The main goal of the project is to use Watson to help identify high-grade areas at Red Lake that were previously overlooked by human eyes.

The mining industry has traditionally been a laggard when it comes to innovation. The 21st-century economy, however, dominated by emerging technologies like electric vehicles, green energy sources and ever-more advanced mobile devices, is demanding creative approaches to efficiently delivering the raw materials that will fuel modern economies. Canadian mining is seeing a real drive to innovate that is bolstering our ability to get materials to global markets as efficiently and cost-effectively as possible. And it's changing what it means to work in mining. New technologies are enabling workers to make quicker, more informed decisions at the front lines of operations.

Oft cited as the blow that is knocking the wind out of the services business, AI has flexed its disruptive muscle. While Uber's advanced algorithms have produced efficiencies in ride sharing and differential pricing that have proved difficult for traditional taxi fleets to compete against, AirBnB is using advanced AI to find the perfect match between host and guest, creating an experience that will shine in comparison to high cost hotel rooms. But AI is more than a disruptive force that will displace businesses or replace workers – the technology is moving mainstream to provide decision support in an increasingly broad range of traditional sectors. A good example of this mainstreet extension of AI can be found in the experience of Vancouver-based Goldcorp Inc., which is using IBM's Watson to optimize exploration. Goldcorp is one of the largest gold mining operations in the world; however, mining in general is characterized as a'high risk, high reward' activity in which ore discovery can have a significant impact on profitability.

If you just include "machine learning" in your pitch you can add a zero on to the end of your valuation. Machine Learning (ML) and the Internet of Things (IoT) are huge buzzwords right now, and they're both near the peak of the hype cycle. The above quote came somewhat jokingly from an investor, but it has some truth to it too. Given all the hype and buzz around machine learning and IoT, it can be difficult to cut through the noise and understand where the actual value lies. In this week's #askIoT post, I'll explain how machine learning can be valuable for IoT, when it's appropriate to use, and some machine learning applications and use cases currently out in the world today.

If you just include "machine learning" in your pitch you can add a zero on to the end of your valuation. Machine Learning (ML) and the Internet of Things (IoT) are huge buzzwords right now, and they're both near the peak of the hype cycle. The above quote came somewhat jokingly from an investor, but it has some truth to it too. Gartner's 2016 Hype Cycle for Emerging Technologies -- Machine Learning is at the very peak of the hype cycle, with IoT Platform and other related IoT technologies on the up-slope. Given all the hype and buzz around machine learning and IoT, it can be difficult to cut through the noise and understand where the actual value lies.

Machine Learning (ML) and the Internet of Things (IoT) are huge buzzwords right now, and they're both near the peak of the hype cycle. The above quote came somewhat jokingly from an investor, but it has some truth to it too. Given all the hype and buzz around machine learning and IoT, it can be difficult to cut through the noise and understand where the actual value lies. In this week's #askIoT post, I'll explain how machine learning can be valuable for IoT, when it's appropriate to use, and some applications and use cases currently out in the world today. And if you're new to these topics, you might want to get up to speed on Machine Learning and the Internet of Things before we dive in!

What are some of the applications and use cases? Machine Learning (ML) and the Internet of Things (IoT) are huge buzzwords right now, and they're both near the peak of the hype cycle. The above quote came somewhat jokingly from an investor, but it has some truth to it too. Given all the hype and buzz around machine learning and IoT, it can be difficult to cut through the noise and understand where the actual value lies. In this week's #askIoT post, I'll explain how machine learning can be valuable for IoT, when it's appropriate to use, and some applications and use cases currently out in the world today.