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Tuesday, February 11, 2020
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Monday, February 10, 2020
Machine Learning: A Valuable Enterprise
Benefits like cost savings and efficiency make ML an MVP
These benefits are especially prominent when coupled with the IoT and industrial markets
Machine Learning is a valuable player in the realm of the Internet of Things. ML and Internet of Things (IoT) have gained tremendous popularity over the past few years, considered by many as revolutionary, game changing tech. Yet, much confusion exists in terms of understanding the purpose of Machine Learning, along with it’s benefits and suitability for use.
Here’s a breakdown of Machine Learning, benefits of ML in AI and IoT, when it should be used, and it’s real-world applications today.
Data Analytics vs. Machine Learning
With all the aforementioned hype around machine learning, many organizations are asking if applying machine learning could benefit their business model. In the vast majority of cases, the answer is a resounding no. In the case of big data, however, Ml may prove very useful.
Machine learning takes large amounts of collected data and generates useful, real-time insights that help the organization based on it’s inherent learning capabilities. That could mean improving vast amounts of processes, cutting costs, creating a better experience for the customer, or opening up new business models.
The thing is, most organizations can get many of these benefits from traditional data analytics, without the need for more complicated machine learning applications.
Traditional data analysis are great at explaining data. You can generate reports or models of what happened in the past or of what’s happening today, drawing useful, insightful conclusions about your organization.
Data analytics can help quantify and track goals, enable smarter decision making, and then provide the means for measuring success over time.
When Is Machine Learning Valuable?
In general, machine learning is valuable when you know what you want but you don’t know the important input variables to make that decision. So you give the machine learning algorithm your stated goals, or inputs. Based on learning systems, and then it “learns” from the data which factors are important in achieving that goal.
The data models that are typical of traditional data analytics are often static and of limited use in addressing unstructured, fast-changing, sequestered amounts of data. When it comes to IoT, it’s often necessary to identify correlations between dozens of sensor inputs and external factors that are rapidly producing millions of data points.
In addition, ML has the ability to accurately predicting future events. Whereas the data models built using traditional data analytics are static, machine learning algorithms constantly improve over time as more data is captured and assimilated. This means that the machine learning algorithm can make predictions, see what actually happens, compare against its predictions, then adjust to become more accurate.
The predictive analytics made possible by machine learning are hugely valuable for many IoT applications. Let’s take a look at a few concrete examples.
How are Machine Learning Applications used in IoT?
Cost Savings in Industrial Applications:
Predictive capabilities are extremely useful in an industrial setting. By drawing data from multiple IoT sensors in or on machines, machine learning algorithms can “learn” what’s typical for the machine and then detect when something abnormal begins to occur.
Predicting when a machine needs maintenance via IoT data is incredibly valuable, translating into millions of dollars in saved costs. A great example is Goldcorp, a mining company that uses immense vehicles to haul away materials.
When these hauling vehicles break down, it costs Goldcorp $2 million per day in lost productivity. Goldcorp is now using machine learning to predict with over 90% accuracy when machines will need maintenance, meaning huge cost savings.
Shaping Experiences to Individuals:
We’re actually all familiar with machine learning applications in our everyday lives. Both Amazon and Netflix use machine learning to learn our preferences and provide a better experience for the user. That could mean suggesting products that you might like or providing relevant recommendations for movies and TV shows.
Similarly, in IoT machine learning can be extremely valuable in shaping our environment to our personal preferences.
The billions of sensors and devices that will continue to power connected devices, smart homes, and IoT devices in the coming years will generate exponentially more data. This huge increase in data will drive great improvements in machine learning, opening countless opportunities for us to reap the benefits.
Not only we will be able to predict when machines need maintenance, we’ll be able to predict when we need maintenance too. Machine learning will be applied to the data from our wearables to learn our baseline and determine when our vitals have become abnormal, calling a doctor or ambulance automatically if necessary.
Beyond individuals, we’ll be able to use that health data at scale to see trends across entire populations, predicting outbreaks of disease and proactively addressing health problems.
Although both machine learning and IoT can be over-hyped, the future of machine learning applications in IoT are worthy of that hype. We’re really just scratching the surface of what’s possible.
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Friday, February 7, 2020
7 IoT Trends on the Rise
Look Out for These 7 Trends in the Internet of Things (IoT)
Trends include the rise of cloud computing, big data convergence, artificial intelligence, smart cities, and more
By Leila Kojouri
The internet has become a beastly enterprise, with the past ten years seeing more technological advancements than perhaps any other industry. The Internet of Things, or IoT, for example, is one such advancement gaining immense traction in the marketplace. Here are 7 IoT trends to look out for this year.
1. Big Data Convergence:
IoT is changing the way we live and conduct business exponentially, generating a huge amount of data in the process. Big data platforms, for example, are usually made for supporting the demands of large-scale storage and for performing investigative work.
Interestingly, IoT and big data have a lot in common. Smart devices, for example, are now being designed specifically for the purpose of digesting massive amounts of system and user generated data. The cloud has proven instrumental in meeting analytic and storage requirements in the realm of big data. Moving forward, the junction between IoT and big data will definitely be a trend on the rise.
2. Data Processing with Edge Computing:
Albeit a powerful force, weaker elements in IoT are evident in the addition of devices behind the firewall of the network. While Securing the devices may be easy, securing the IoT itself proves a trickier phenomenon. Thus, security measures must exist between the network connection and the software applications linking to the devices.
Perhaps the most notable benefits of IoT are it’s cost-effectiveness and efficiency, especially in data processing. Rapid-fire data processing is prominent in most smart devices, re: self-driving vehicles and intelligent traffic-lights (aptly coined, “smart lights”). Edge computing has been proposed as a potential solution, gaining immense popularity.
Edge computing usually outperforms the cloud when it comes to speed and cost. Faster processing translates to lower latency, which is one of the premium benefits of edge computing. Data processing with edge computing will see an uptake in IoT trends in the near future, for certain.
3. Auto-ML (Machine Learning) for Data Security:
In present days, developers are tasked with finding newer methods in which people can share data securely by the use of block-chain-like technologies. Many industrial companies are learning how to trust and accept the forecast of machine learning models (otherwise known as “predictive analytics”), and will acclimatize their operations for preventing the downtime by model outputs.
Machine learning model training will likely become a highly automated process. Industrial companies in particular will increase the large capital assets, particularly in cloud computing, in the near future.
4. IoT – Massive Growth Coming:
When it comes to data analytics, IoT is perhaps the most promising technology to date. Smart devices ingest more data and information about the devices and users, and by 2020, it is expected that IoT devices will exceed 31 billion. Today, we see IoT devices as the major part for reporting and tracking.
IoT is capable of the extraction, storge, and analysis of massive data stores. When coupled with other ground-breaking technologies, like Artificial Intelligence (AI), for example, the essential data can be appropriately measured, filtered and categorized. IoT trends will most definitely see an increase in the amount of “work” smart devices are doing. What’s more, they will serve to assist data scientists and technicians in providing powerful, insightful suggestions.
5. Better Data Analytics:
Chances are, you’ve heard some of the hype and clamor surrounding artificial intelligence in modern business practices. The merger of IoT and AI has seen many recent developments, as the two can be used interdependently to provide e a centralized decision-making tool for all types and sizes of businesses.
AI and machine learning are systems that can easily identify trends, patterns, and unique behaviors otherwise invisible to the naked human eye. Better data analytics and the need to safeguard this said data are two major reasons for the rise of this trend. By collecting insights from this data, we could even suggest it be rendered to help us make better decisions in our personal lives. The sheer intelligence, thoughtfulness and self-learning capabilities make this a hugely popular trend to look out for in the near future.
6. Smart Cities to Become Mainstream:
When it comes to data collection, many states have adopted a more technological approach – replacing or improving upon antiquated infrastructure; integrating sensors to reap data that proves invaluable for the purpose of urban planning and development. Prepare yourself for the integration of IoT into just about every sidewalk, crosswalk, highway, and byway, as data collection becomes less of a convenience and more of an evolutionary imperative for American progress.
Cities both nationwide and globally will become pioneers for the great data exchanges affording accessibility and empowering evidence for better decision-making. Ultimately, the digestion and dissemination of this unique, all-telling data will provide a fundamental platform for both private and public organizations, and the citizens under their watch.
IoT integration for the sake of developing “responsive cities” can accomplish goals such as lessening traffic congestion, unlocking sustainable development and improving safety precautions.
7. Personalization of the Retail Experience:
With IoT, the efficiency of supply chain and information systems management has grown by leaps and bounds with respect to retail. Sensors and other smart beacon technologies are being used to tailor shopping experiences with ease, speed, and accuracy like never before.
In the not-so-distant future, IoT can be used to monitor, gauge, track, and personalize your investment portfolio – making unique, custom trade recommendations based on your data insights. Or, imagine getting notified immediately when a highly-sought after product from your favorite shop is discounted via push notification which when expanded, offers an indoor map of your favorite shop – leading you to the exact product you desire.
The value of this trend ensures the better integration of personalized retail experiences which ultimately can bring upon a new era of shopping as we know it.
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