The internet of things (IoT) is a network of dedicated devices -- called things -- deployed and used to gather and exchange real-world data across the internet or other networks. Where an enterprise routinely deals with documents, PowerPoints, images, videos, spreadsheets and many other forms of static digital information, IoT devices produce data that typically reflects one or more physical conditions in the real world. IoT devices can not only help a business to learn what's happening, but also exercise control over what's happening. Where routine data -- such as a memo document -- can exist for days or months without ever being used, IoT devices must deliver data for collection and processing without delay. This makes related factors, such as network bandwidth and connectivity, particularly important for IoT environments.

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In an ideal world, organizations could easily plan machine learning for IoT with the right technical experts, and components would perfectly integrate with each other. Unfortunately, no matter how well a team researches IoT machine learning before it starts development, some of their assumptions will be proven wrong and they will face unexpected challenges. During her IoT Tech Expo 2020 presentation, "Building Machine Learning Products -- a Best Practice Approach," Jenn Gamble, data science practice lead at Very, identified the required skills to implement machine learning with IoT and how teams can adopt best practices, approach software development and handle unexpected difficulties. "A lot of the data science development lifecycle is actually very different than what the Agile software development lifecycle is. A lot of those hard-won best practices from software engineering, the data science community was not always aware of, or, at least, not fully embracing or benefiting from," Gamble said.

Artificial intelligence is a vital component of digital transformation initiatives as the volume of data that organizations gather continues to swell. A total of 69% of survey respondents to the 2020 IoT Adoption Survey reported using artificial intelligence (AI)/machine learning (ML) as part of IoT initiatives. Top AI priorities include performance monitoring and trend forecasting in addition to sensor and business data integration. Other popular applications of artificial intelligence in IoT projects include ensuring security, computer vision and product design and testing. Those findings are consistent with other data.

The internet of things (IoT) has huge potential. According to one forecast, there will be a trillion connected computers in the world by 2035, built into everything from cars to toasters. Collectively, such IoT devices, which record, monitor and communicate data, herald what some call the "second phase of the internet". Companies at the forefront of IoT are set to be the winners of the future. But we have yet to see what industries and uses will benefit the most.

Companies are increasingly embracing the internet of things (IoT), but IoT initiatives on their own are falling short when compared to those that pair the technology with artificial intelligence (AI). This is according to a survey conducted at the IoT Solutions World Congress by SAS, Deliotte and Intel, which found that pairing the two technologies is yielding significantly improved benefits for businesses over IoT alone. In the survey of business leaders, 92% of those involved in IoT projects said that involving AI had resulted in benefits above expectations. When it comes to using the technology for daily planning, only 12% of organisations found IoT on its own to be beneficial, but when adding AI this number increased to 31%. This improvement was also found when applying the technology to operational speeds. For a third, combining the technologies was done primarily to increase revenues.

Leaders looking to make the most of the opportunities inherent in these linked technologies need to identify specific business goals -- then chart out how to realize those goals using the surfeit of data that IoT devices produce and AI analyzes. Forbes Insights surveyed hundreds of business leaders about their IoT and AI strategies. The results show how leaders are making the most of their advantages, and provide a blueprint for those looking to challenge themselves and to grow. The first task for enterprises looking to accomplish more with AI and IoT should be to closely examine how committed they are to them in the first place. The fact is, most firms have yet to fully engage with transformative technologies.

Artificial intelligence (AI) will act as an enabler and help accelerate IoT projects, according to VMware's IoT lead. To that end, VMware will work to provide both AI-enabled infrastructure technology as well as leverage AI to optimize the technology it develops and delivers to customers, according to Mimi Spier, vice president of IoT business for VMware. "The ability to get data out of things and the amount of impact you can have when you start to use IoT and AI together is quite profound," Spier said. "For the future, to achieve the picture I just painted, you have to have a flexible, manageable, scalable, secure foundation that enables us to do things on the edge … as well as deeper learning in the cloud." Spier lent her impressions on AI and IoT projects during a recent interview around a PWC report that shed a positive light on AI and job creation, with the report's authors concluding AI-enabled technologies will generate as many jobs as they displace overall, when evened out across different sectors. The recent report comes at a time when businesses are still finding that launching IoT projects is hard, and it's taking longer than many in the industry expected, according to Spier.

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NVIDIA has announced a partnership with Internet of Things (IoT) chip designer ARM, aimed at advancing the acceleration of inferencing by making it simple for IoT chip companies to integrate artificial intelligence (AI) into their designs. During his keynote at NVIDIA GTC in San Jose on Tuesday, company CEO and founder Jensen Huang explained that the partnership will see the open source NVIDIA Deep Learning Accelerator (NVDLA) architecture integrated into Arm's Project Trillium for machine learning. NVDLA is based on NVIDIA Xavier, touted by the GPU giant as being a "powerful autonomous machine system on a chip." According to Huang, this will provide a free, open architecture to promote a standard way to design deep learning inference. "Inferencing will become a core capability of every IoT device in the future," NVIDIA vice president and general manager of Autonomous Machines, Deepu Talla, said during a press briefing.