Earlier this May I wrote that, following a year where the internet of things found the mainstream, 2017 would be the year for IoT acceptance, appetite and evolution. Key trends highlighted in the article, like security, strategic partnering and public sector uptake, were all central to the Internet of Things World 2017 agenda; three themes covered by FBI CISO Arlette Hart (among others) in her keynote panel session and subsequent interview.
Of all the trends covered, the proliferation of big data and machine learning in conjunction with the growth of IoT across businesses seems the most exciting. The internet of things means nothing to anyone without the data it harvests; the next generation of business will be defined by the way in which companies apply artificial intelligence to this and analyze the data they collect. By 2019, Cisco expects IoT devices to be generating 507.5 zettabytes of data annually (the analyst estimates the world’s collective internet usage only hit 1 zettabyte in September 2016). These are gigantic data streams offering a virtually limitless number of insights.
Active examples of enterprises applying IoT data and machine learning to great effect already exist, with case studies from the Bay Area and beyond demonstrated at IoT Data & AI Summit in Palo Alto back in November. Here are four use cases that should grab the attention of all technology-oriented enterprises:
- Marketing and search engine applications. Right now, IoT and AI are combining for a more searchable and shoppable internet. eBay is leading the way, building on a concept first announced during its Hack Week in 2015. Find It On eBay allows users to upload images to the site from elsewhere on the internet or social media and run a product search without typing a word. This next-gen technology has wide-reaching implications for search marketers and the retail industry.
- Chatbots to transform customer service. AI chatbots are the next logical step in the world of customer service. Until now, making chatbots appear simultaneously intelligent, convincing and sincere has proven challenging. That’s why MindMeld is building AI systems where the number one priority is to hold a “natural” conversion. Once the company cracks that, one of the hardest obstacles faced by chatbots will have been overcome.
- Bringing AI to ride-sharing. San Francisco’s own Lyft had a big self-driving car breakthrough recently when it announced a partnership with drive.ai. It wants to bring more autonomous ride-sharing to the Bay Area as soon as possible — so much so that it’s developing retrofit kits for consumers that can be added to existing vehicles. It’s one of the many areas of consumer transportation undergoing disruption, with IoT Data & AI Summit speakers Ai Incube applying machine learning to rank roads by your chances of finding a parking spots citywide, and a host of other use cases promising widespread sustainability and convenience.
- Cybersecurity that mimics the human immune system. Darktrace is a machine learning company with a truly novel approach towards security IoT networks. The company has developed a platform that applies unsupervised AI algorithms that defend enterprise systems in a manner inspired by the human immune system. The AI autonomously searches for threats and breaches, detects them and evolves its own capabilities. It’s detected 53,000 previously unknown threats through doing so.
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