As the internet and data sets become more powerful, we can expect that companies will choose to use machine learning models to solve some of the most basic business problems.
Machine learning is rapidly changing the face and pace of business as we know it. On one hand, we see a mountain of promises made by technology companies that machine learning will make life easier for all. At the same time, there is a part of the population afraid of machine learning, particularly when it comes to job availability. We will explore machine learning and find out how it can realistically support business.
What is machine learning?
Machine learning is part of the umbrella of technology widely known as artificial intelligence (AI) focused on creating systems that learn from historical data, identify patterns in learning, and make logical decisions that require little to no human interaction. In short, it is a method of data analysis involving a variety of digital information, i.e., numbers, words, clicks, and images.
Machine learning applications are able to learn from data input and continue to improve upon the accuracy of the output with the use of automated optimization methods. The overall quality of a machine learning model depends upon the following:
#1. Machine learning requires high-quality input data.
Much like a garden requires quality fertilizer to grow, a machine learning model requires high-quality data to get the best outcome. Low-quality, or inaccurate data, will yield a poor output.
#2. Machine learning requires a high-quality machine learning model.
There are a ton of algorithms a data scientist can choose to meet their needs. It is important to choose the algorithm best suited for each use case. More complex neural networks are popular for some algorithms because they tend to be more accurate and versatile. However, a simpler model will often perform better when using a lower amount of data.
Starting with a proven machine learning model is imperative because it is more likely to accurately find features and patterns in data. The better the data, the better decisions and predictions the machine will be able to make.
Why is machine learning important to modern business?
Machine learning is growing in popularity due to three factors:
These factors make it easy for companies to develop computational models that can quickly and accurately analyze super complex data sets.
Machine learning is being used to cut costs, minimize business risk, and improve quality of life. This can include making product recommendations, exposing potential cybersecurity threats, powering self-driving cars, and even labeling an X-ray as cancerous or not. As time moves forward, we are sure to see more examples of how machine learning can improve life across the spectrum.
But what can machine learning do, realistically, to help move technology forward? We can start by busting a few of today’s machine learning myths.
Myth #1: Machine learning is more intelligent than humans.
There is no doubt about machine learning’s powerful ability to find patterns and correlations using available data sets. However, at this point, humans are still needed to intervene to make assessments on the quality of results.
Using the example of a medical diagnosis, machine learning is able to quickly review available data. However, doctors and supporting medical professionals are needed to rule out inconsistencies in findings.
Myth #2: Machine learning will take over jobs.
While modern industry is seeing more robots automate manual work in places like factories, production facilities, and medical surgeries, the implementation – at this moment – is more of an assistive technology and not a replacement for human minds and hands. In fact, machine learning has made modern business practices more efficient via the simplification of repetitive processes.
Myth #3: Machine learning never changes.
Cybersecurity is a great example of how machine learning is always expanding out of necessity. The machine learning algorithms of today’s cybersecurity environment will no longer work in the next few weeks to months ahead. Why? Because criminals are always finding new ways to overtake technology for their own purposes. While machine learning models may be routine in a factory or warehouse, cybersecurity machine learning models will always have to be built from scratch.
Myth #4: Machine learning requires more data to get reliable results.
If you are a data scientist, it may make sense to add more data points to a machine learning model. This may not always be the best use of data. If an enormous amount of data is dumped into a machine learning model, there is risk in creating a model that memorized the information, leading to a case of model overfitting. This can also result in high error rates for unseen data. Your machine model needs that garden-fertilizer data for high-quality output. It also requires high-quality data to have a better chance at building the best machine learning model.
Myth #5: Machine learning can predict the future.
It is partially true companies can use machine learning to predict the future. But machine learning models can only predict the future if future events have some relevance or connection to past events. For example, there are some machine learning models that use past stock prices to predict future stock prices. Also, the weather can be predicted based on past weather information. Yet if a machine model is asked to make a prediction based on information that was not input prior to the development of the model, the prediction will not be dependable.
The use of machine learning is expected to grow. As the internet and data sets become more powerful, we can expect that companies will choose to utilize machine learning models to solve some of the most basic business problems.
Find out when you attend the Jan. 11, 2023 webinar presented by Terry Wohlers.
As the additive manufacturing (AM) industry continues to mature, many companies are looking to AM for where it can help shorten lead time and create new opportunities. However, AM is not a one-size-fits-all solution and still isn’t right for mass production in many areas. With AM being used for an array of applications, it can complement a company’s capabilities for some production applications.
Register for this free webinar – Advances and challenges in additive manufacturing – taking place on Wednesday, Jan. 11, 2023 12:00PM ET.
Learn about many of the most interesting developments in the industry, while recognizing the challenges that many face. Hear how AM can be scaled for mass production – and where that’s happening today. And find out some of the most thought-provoking trends in the AM industry.
Registration is now open for Automate, the place to help companies find the latest innovations in robotics, machine vision, motion control, and industrial artificial intelligence (AI).
Automate, is back in Detroit May 22-25, 2023, with the latest in cutting-edge robotics, vision, artificial intelligence, motion control, and more. Produced by the Association for Advancing Automation (A3), Automate delivers the latest innovations in manufacturing automation technology from more than 600 leading exhibitors. Each day also offers inspirational keynote sessions and theater presentations to help attendees find the best solutions for their unique business needs. Show registration is free.
The paid Automate Conference will feature accomplished industry professionals in over 150 talks. Those looking to sharpen their skills or deepen their automation knowledge should sign up for these educational conference sessions to get practical solutions, discover the latest innovations or learn from real-world case studies.
“Now is the opportunity for companies to take their business to the next level at the most important automation event in 2023,” says Jeff Burnstein, president of A3. “Automate features automation applications to meet any business need, whether in manufacturing, logistics, consumer goods, food processing or any number of other industries. It’s where you’ll learn how to power your productivity, become more efficient, gain a competitive edge, and future-proof your business for years to come.”
Automate 2023 will be the biggest yet, featuring 300,000ft2 of exhibit space. Limited exhibiting spaces remain.
Experience real-world solutions, valuable educational sessions
In addition to seeing demos of the latest automation solutions, Automate show attendees can watch keynote sessions highlighting how these technologies solve real-world challenges or participate in small group discussions in the theater sessions covering important topics such as how robotics and automation are transforming the economy; innovative strategies for jumpstarting an automation strategy, or how companies can cultivate talents in the workforce. This year will again feature presentations from the finalists of the Automate Startup Competition, sharing why their new offering should win the cash prize – and credibility.
At the Automate Conference, held alongside the free show offerings, paying attendees will hear from experts in the industry and can get practical training to become a certified vision professional or certified motion control professional with courses and testing available on-site.
“Over 20,000 people from around the world will come together at Automate to demonstrate what’s possible in automation today and in the future,” Burnstein said. “If you’ve ever wondered if these technologies are the right choice for your business, register free and let us show you how robotics, vision, AI and more can unlock higher product quality, lower costs, and unlimited possibilities. The opportunities are endless. We look forward to helping you get what you or your business needs from Automate next May!”
December’s Manufacturing Lunch + Learn, taking place at 12PM ET 12/15/22, features Joseph Pizzoferrato, TNC product specialist & application engineer at Heidenhain.
The digital world of manufacturing consists of 3 industry standard protocols: OPC UA, Modbus, and MT Connect. These standards make up how we communicate with the machine-tool world. Once you understand what protocol you want to use then you must decide how you want to visualize your data i.e., build your own system or use an existing system
Nowadays everyone has monitoring software to monitor machine tools but it’s how you use the tools inside that help you drill down to make progress. When you join this webinar, Joe will give a brief introduction into the software’s diagnosis tools as well as explain other tools you can use in the digital world such as the digital twin and remote monitoring to leverage the digital world in your current manufacturing process.
Registration is free for the December Lunch + Learn so sign-up today!
Leveraging the International Space Station National Laboratory, researchers are testing the implant's ability to be controlled in space from a device on Eerth.
A new remotely controlled drug delivery implant could one day provide extended, adjustable medication for patients who need daily medicine but lack medical access – even those on spacecraft headed for Mars. Houston Methodist Research Institute researchers have developed such an implant. They are leveraging the International Space Station (ISS) National Laboratory to test the implant’s ability to be controlled in space from a device on Earth.
The investigation, launched on SpaceX’s 26th Commercial Resupply Services mission (SpaceX CRS-26), will lay the groundwork for future experiments from the research team involving rodent models on the ISS. The goal is to improve the implant’s ability to transmit signals to Earth and ensure the drug delivery system is safe for humans, says Alessandro Grattoni, professor of nanomedicine at Houston Methodist Research Institute.
“We’re preparing for the first demonstration of a remotely controlled telemedicine implant in an animal model on the ISS. It’s the ultimate sci-fi medicine in space,” Grattoni says. “And beyond our investigations, the implant could provide a valuable technology for drug dosing in rodent research studies with no need for astronaut time.”
The implant uses nanofluidics technology that combines membranes with very small nanochannels to deliver a controlled drug dose through diffusion. This investigation builds upon the research team’s previous ISS National Lab-sponsored experiments that studied fluid flow through nanochannels to design the implant’s ability to release specific amounts of drugs for individualized treatment. Grattoni also led an investigation that tested the implantable nanochannel drug delivery system in a rodent model to assess its ability to mitigate microgravity-induced muscular atrophy.
For this current investigation, Grattoni and his team will test their remotely controlled implant in an automated experiment using the Faraday Research Facility developed by ISS National Lab Commercial Service Provider ProXopS, LLC. The implant contains computer chip-like technology and is immersed in liquid saline to represent a “surrogate animal model.” The researchers will attempt to control the implant using Bluetooth and a Blackberry device on Earth to test different frequencies to determine if the implant can precisely deliver and adjust doses on command.
The investigation’s results will serve as a critical step toward the implant’s future use in space travelers who may need safe, automated access to a medication that requires frequent dosing. For example, a therapeutic currently being developed may one day help mitigate the effects of radiation or prolonged exposure to microgravity in space.
“Radiation exposure is a limitation for taking people to Mars, and even the Moon,” Grattoni said. “Other precautionary methods are being developed to prevent radiation exposure, but our device will be the first that a doctor back on Earth could use to instantly deliver medication to treat an exposed astronaut during a long-term mission.”
Grattoni’s team has also designed implants with multiple reservoirs that allow different drugs to be dosed simultaneously. The implant can even be pre-programmed, facilitating its function when communications are not possible or delayed because of distance from Earth. Eventually, Grattoni says, doctors on Earth will be able to control drug delivery implants wherever patients are located—from remote locations on Earth to distant space—using an application on their smartphone or computer.
This is one of more than 20 ISS National Lab-sponsored payloads launching on SpaceX CRS-26, which is set to launch from Kennedy Space Center no earlier than November 21. Please visit our launch page to learn more about all ISS National Lab-sponsored research on SpaceX CRS-26.
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