The stuff of science fiction for decades, artificial intelligence (AI) and its sub-sectors like machine learning (ML), deep learning (DL), and natural language processing (NLP) have arrived in the real world.
It’s not just computer science wonks and sci-fi writers who are paying attention. As AI enters more use cases and is deployed in more situations, it’s attracting more interest from investors who can see the profit potential in AI applications.
Manufacturing companies have long been gathering masses of data within the plant, but advanced AI means that they can finally derive real value from it. Predictive analytics uses ML and DL to spot patterns within data and forecast likely consequences and scenarios.
For example, predictive monitoring solutions reduce the need to send humans into hazardous conditions to check on equipment and processes, while producing earlier warnings about anomalies. With earlier alerts, plant employees can correct issues quickly and at a lower cost, preventing them from evolving into serious incidents.
Predictive maintenance uses a similar process to refine maintenance scheduling, ensuring the plant operates at peak condition, which in turn results in less waste, higher quality product, less unplanned downtime, and thus higher profits.
Moving forward, we’re beginning to see factories adopt AI-based machine-to-machine (M2M) communication, enabling robotic process automation (RPA) for lights-off production. AI-automated processes reduce the time it takes to reconfigure equipment for a new batch, while also lowering the risk of human error.
AI stocks offering manufacturing solutions like predictive monitoring and RPA systems are benefiting from companies that want to stay agile, prepare for the next disaster, and keep their competitive edge.
Customer data floods into businesses across every vertical. Leading enterprises apply AI to understand what customers want, predict emerging trends, and meet expectations.
For example, airlines are using AI to develop more relevant offers. Business-to-business (B2B) companies are applying it to understand which features to add to their solutions; even manufacturers are forecasting demand. Retailers have found that AI enables them to improve stock levels and offer more personalized products and suggestions, and some bricks and mortar stores are experimenting with integrating AI analytics with GPS in smartphones, to send a push notification when a customer passes the store.
AI is the basis for extended reality (XR) retail experiences, many of which have already arrived. Virtual reality (VR) dressing rooms use deep learning to create a 3D model of your body, so you can “see” how clothes will look before you order them online. VR apps allow you to check how furniture will look in your living room before it arrives, and VR makeup applications let you test out a new look from home.
Businesses deploying AI enjoy a deeper understanding of their customers and greater insights into emerging trends, so they can lead the field in customer experience.
Some profitable AI applications are focused outside the world of business. Cities are using AI to analyze road accidents, so they can understand the impact that different elements of road design have on pedestrian, cyclist, passenger, and driver safety.
Meteorologists apply DL models to meteorological data, to predict the path of hurricanes, tropical storms and tornadoes more accurately. With the help of AI, experts can issue alerts about flood warnings and extreme weather events further in advance, reducing damage to life and property.
AI can crunch data from drones and sensors to understand the extent and impact of earthquakes and predict volcanic eruptions, as well as creating virtual models of wildfires to guide firefighters to work more safely and effectively.
We’re still only scratching the surface of AI applications in healthcare, and what we’ve uncovered so far is already awe-inspiring.
Physicians use AI to help interpret complex scans like mammograms, where false positives can be as high as 1 in 2. ML diagnostics can be better at spotting rare diseases than a physician. The doctor may have barely heard of a rare condition, but an AI platform can compare symptoms against millions of case studies and health records around the world.
Today’s wearable health monitors produce valuable data, but datasets are too large for human analysis. AI can quickly learn what is “normal” and what requires an urgent response. Combined with big data, AI engines can spot the early indications of conditions like heart disease and diabetes long before a human notices.
AI is also being applied by healthcare providers to understand the needs of their users, so they can make better decisions about investing in equipment and human resources, and deploy those resources to better effects.
Finally, health researchers are using AI to speed up the process of developing and testing new treatments.
With the help of AI engines, banks, insurance companies, credit unions, and other financial institutions are accessing new and more accurate methods of fraud prevention. AI can spot the patterns that indicate a potentially fraudulent credit card transaction, for example; anomalies in an application to open a bank account; and detect and track money laundering.
Lending companies can use AI to scan millions of datasets, identifying people at the greatest risk of defaulting on a loan. With these insights, they can make faster loan decisions that are more accurate than those based simply on credit scores.
Consumer-facing businesses, especially ecommerce companies, are also turning to B2B AI engines that can pick up on suspicious transactions. By putting them on hold while they investigate further, businesses can lower their risk of damaging chargebacks.
For investors, the many and growing number of use cases for AI across so many different sectors make a powerful argument. And investing in AI offers the opportunity to go beyond pureplay AI stock; quantum computing stocks, which deliver the high compute power that makes AI analysis possible, are also attracting attention. The decision about whether to invest in quantum computing may be complex, but the potential to deliver AI results is undeniable.
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Defiance Quantum ETF (QTUM)
Investing involves risk. Principal loss is possible. As an ETF, QTUM (the “Fund”) may trade at a premium or discount to NAV. Shares of any ETF are bought and sold at market price (not NAV) and are not individually redeemed from the Fund. The Fund is not actively managed and would not sell a security due to current or projected under performance unless that security is removed from the Index or is required upon a reconstitution of the Index.
A portfolio concentrated in a single industry or country, may be subject to a higher degree of risk. The Fund is considered to be non-diversified, so it may invest more of its assets in the securities of a single issuer or a smaller number of issuers. Investments in foreign securities involve certain risks including risk of loss due to foreign currency fluctuations or to political or economic instability. This risk is magnified in emerging markets. Small and mid-cap companies are subject to greater and more unpredictable price changes than securities of large-cap companies.
The value of stocks of information technology companies are particularly vulnerable to rapid changes in technology product cycles, rapid product obsolescence, government regulation and competition. The possible applications of quantum computing and machine learning are only in the exploration stages, and the possibility of returns is uncertain and may not be realized in the near future.
Opinions expressed are subject to change at any time, are not guaranteed, and should not be considered investment advice.
Read more about QTUM here, including performance and current holdings: https://www.defianceetfs.com/qtum/. QTUM is distributed by Foreside Fund Services, LLC.