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How AI Is Bringing Crucial Machine Intelligence to IoT

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How AI Is Bringing Crucial Machine Intelligence to IoT

Let's look at how Artificial Intelligence in bringing crucial machine intelligence to the Internet of things.

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We find ourselves in a pivotal era in technology, where the things we've been promised for years like augmented reality, advanced 3D printing, artificial intelligence, and autonomous vehicles are becoming a reality(albeit not necessarily in the forms we'd like them yet, or in all locations or price points). As technology evolves, technologists across disciplines and industries are sharing knowledge and research, leading to even more discoveries and cross-practice convergence.

At the nexus of this evolution is the Internet of Things. Its ubiquity may receive the most attention in consumer and residential products, but the validation of the ability to change things for the better is evidenced most strongly in Connected Industry. But, smart consumer devices are just starting to catch up. Add artificial intelligence to the mix and things really start getting interesting:

The Benefits of Artificial Intelligence

It's easy to see AI as something of a party trick, (like the dog/muffin pic), a killer robot (not such a great party as we thought), or a buzzword that rolls off the tongue of a startup CEO like something from a checklist. In reality, artificial intelligence has achieved a wide range of capabilities from image recognition to language processing and data analysis. It enables machines to make decisions and deductive reasoning faster than humans. It creates an extra layer on what IoT can achieve, almost an inter-dependence, such as in the growth of AI-powered analytics platforms for the enterprise market and the enablement of predictive and prescriptive analytics and adaptive/continuous analytics.

The sheer deluge of data emanating out of connected devices can add little value to anyone is it is simply stored and collected without rigorous analysis. AI-powered analytics enable the division between time-sensitive data that is processed at the edge (such as that of a piece of connected safety equipment) and more extraneous data that can be processed in larger volume and with less urgency in the cloud.

Each year, what it makes possible succeeds that of the proceeding ten years. We can expect that AI will lay the foundation for an acceleration in innovation over the next few years, boosting some sectors of the economy and completely restricting some industries. While programmers still control the capabilities of AI at present, this may not be the case forever. Let's take a look at some of the key use cases where the convergence of IoT and AI are leading to ever bigger resolutions than the technologies alone could ever achieve.

Precision AgTech

The world of agricultural technology, or agtech, is rapidly evolving.It's automating laborious tasks from seed sowing to crop picking, and picking up the slack in roles where farmers are struggling to recruit seasonal staff. It's also providing farmers and growers with greater knowledge and insight into their crops and livestock than ever before.

Notably, around 20% of the world's food production is grown within cities rather than rural areas, and inherent in this is the multi-billion dollar industry of indoor growing and hydroponics. The industry includes $5 billion in urban farming in the US and $5.7 billion for legal cannabis production.

Today, farms can leverage IoT to remotely monitor soil moisture, crop growth, smart connected harvesters, and irrigation equipment. Then farmers can analyze operational data combined with third-party information, such as weather services, to provide new insights and improve decision-making.

Farmers and growers who have long relied on almanacs for historical references of their crops can now enjoy the ease of a tablet or mobile phone. For example, at the Pago Aylés winery, in collaboration with measurement agricultural scientists from remOT Technologies, has invested in an IoT project with Libelium technology to gain efficient production and a predictive vineyard management model. Strategically placed sensors were installed to measure:

  • Temperature, humidity, and environmental pressure.
  • Soil temperature.
  • Ground humidity.
  • Rainfall, wind speed, and direction.

Winemakers are then able to access the data on their smartphones at any time through a corresponding app which, when combined with historical data, enables growers to establish patterns and predictive models on the behavior of the vineyard. The use of AI means faster data collection and processing and the data collected means they are not only able to be more precise about watering, but also to accurately predict their yield and anticipate the demand before forecasts of purchases or sales according to the estimated production.

Predictive Maintenance in Manufacturing

One of the most compelling aspects of industrial IoT is the ability of sensor technology to solve problems that have plagued traditional industries for years, if not decades. One such problem is machine maintenance and repairs. Up until recently, such maintenance was typically time-consuming, costly, and limited by the challenge of finding appropriately skilled workers. A broken machine could take hours or even days to fix (especially if parts needed to be sourced), and the downtime could result in as high as millions of dollars worth of sellable products.

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Machine maintenance is traditionally a laborious process that requires sending a physical person around each factory/plant/workplace to inspect individual machines, typically on a set schedule. A combination of sensors and AI offer a more effective solution. One example is the work of 3DSignals. They utilize sensor tech to monitor machines through sound. Their system can extend to a range of machines "based on the knowledge of how similar machines are supposed to sound and also learning the very specific sound acoustics of specific machines."This all leads to increased efficiency in maintenance and the ability to predict problems so that an engineer can respond as needed rather than only within a pre-existing preventive maintenance time frame.

3DSignals' acoustic monitoring and deep learning technology monitors sensory data from production line machinery, identifies anomalies, classifies patterns of equipment failure, and predicts issues before they interrupt production. This reduces downtime and saves significant time and money in lost production.

Your Personal IoT Is Finally Getting Smart, Thanks to AI

What if there was a way to make connected products personal, instinctual, and truly targeted to the individual? Start-up Neura has created an AI engine that turns IoT environments into connected universes that allow companies to connect with their customers during the most meaningful moments. Neura enables consumers to take control of their smart home devices — Amazon Echo, Nest thermostat, Hue Lights, Ring Smart doorbell, refrigerators, and more — and make their smart homes more intelligent with the integration of true AI. Their AI engine integrates with multiple data channels to provide situational awareness for individual customers, ensuring personalized, highly relevant engagement.

Neura's tech relies on the reality that the users of IoT tech are surrounded by connected devices throughout most of their day, whether WiFi at work, a smartwatch, a Bluetooth headset, or their connected car. Because of this, an endless flow of connections with their specific patterns and clusters is exposed through predefined moments. Neura's cognitive computing techniques find the patterns that define the users' daily lives to detect and understand the relations between users and their surroundings, their work and home routine, exercise habits, etc.and provide companies with a simple to consume API.

The Neura SDK is integrated into an app and that starts pulling the data from the phone's sensors as well as WiFi and Bluetooth signals. This then feeds into a hybrid AI engine. Therefore, the majority is cloud-based and a lightweight version is in the SDK. Your products access specific insights and predictions via API calls like that you've gone running or left the house. The lightweight AI engine is able to react in real time to an anomaly and change the consequential actions. For example, if you usually go to bed at 10 PM and tonight you're awake, drinking in a bar, the things that usually happen (thermostat change, door locking) aren't going to occur because you're not where you're usually are.

It's easy to be cynical about AI and to question its validity as more than a marketing buzzword. But the reality is that its use, when combined with other technologies such as IoT, is making ever greater advancements possible. Its evolution is coming at us fast and hard and we'd better we'd better keep an eye out if we want to keep up. Furthermore, we must stay alert if we want to be part of the decision making.

This article is featured in the new DZone Guide to Artificial Intelligence: Automating Decision-Making. Get your free copy for more insightful articles, industry statistics, and more!

Start coding something amazing with the IBM library of open source AI code patterns.  Content provided by IBM.

Topics:
artificial intelligence ,iot ,machine learning ,precision agtech ,predictive maintenance ,benefits of ai

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