These organizations use AI to reshape their operations in surprising ways
From online dating to cybersecurity, AI regularly works behind the scenes in various aspects of our daily lives.
From smart infrastructure networks to reports written by robots, algorithms and artificial intelligence capabilities regularly operate behind the scenes in various aspects of our daily lives. COVID-19 has only accelerated the adoption of automation across industries and Gartner called it “smarter, responsible [and] scalable AI “as one of its top data and analytics technology trends in 2021. In this summary, we’ve highlighted some of the ways AI is transforming everything from conversational efforts on animals to twinning in the digital age.
Farming company AppHarvest uses a number of transformative practices to reimagine agriculture in the 21st century, including AI. The company uses computer vision and AI to help its robot harvester, Virgo, pick ripe produce straight from the vine.
The robotic harvester uses a suite of cameras and an infrared laser to map its working environment and uses this information to assess the orientation of a tomato and determine if it is “ripe enough to pick,” according to a press release from the company. These analyzes allow Virgo to determine the âspace-saving and fastest routeâ to pick the produce using its integrated gripper and arm.
In August, the fruit and vegetable harvesting robot developed new dexterity skills while picking strawberries and cucumbers. (Previously, Virgo was shown picking tomatoes from the vine in other videos.)
âWith robots roaming the facility, interacting with and caring for the crops, we will continuously collect data on crop production to power the AI, then use software to align facility operations with sales. and logistics, making agriculture as reliable and predictable as a factory, âWebb said at a recent AppHarvest results meeting.
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Automation and computer algorithms could also transform the roles of humans in the financial services industry. As we reported earlier this year, “bots” were more reliable than people when it came to managing money, according to an Oracle study released in February. Overall, the vast majority of business leaders surveyed (85%) wanted âthe help of robots with financial tasksâ and about half (56%) believed that robots âwould replace financial professionals in the financial sector. ‘business’ over the next five years, according to the study.
âAI and machine learning are increasingly prolific in almost every area of ââbanking, from back office applications and customer engagement to compliance,â said Jason Somrak, chief product officer and of strategy at Oracle Financial Services.
Noting specifically financial crime and the fight against money laundering, Somrak said these are areas where these applications “have a huge impact”.
âWhile traditional rules-based AML scenarios may keep financial institutions in technical compliance, they are unable to accommodate the ever-changing models of today’s criminals,â Somrak said. “However, more and more people are starting to take advantage of technology to identify a criminal’s digital fingerprint.”
Using historical and current data, Somrak said machine learning algorithms are constantly learning, which can “identify recurring or changing criminal behavior” to “connect suspicious money flows between criminal organizations. “. On the âemerging AIâ front, Somrak discussed the âdeployment of intelligent artificial agentsâ to help identify gaps in an organization’s compliance controls.
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Researchers around the world are harnessing a wide range of technologies to aid in wildlife and land conservation efforts. This includes using facial recognition to track bears in Canada to predicting wildfires through deep learning. As we reported in December, a team of researchers in Australia are using AI-powered drones to protect the iconic koala from habitat destruction and more.
To increase the efficiency and accuracy of the koala count, Grant Hamilton, associate professor of ecology at QUT, and his team have developed a methodology that uses drones, thermal cameras, and AI. But why is it important to count koalas?
âHow do we know that our management actions are having any effect? Well we must be able to count [the koalas]. Thus, counting these endangered species is fundamental to ensure that they are preserved. Unless we can do it accurately and efficiently, it won’t happen, and that’s the problem right now, âsaid Hamilton.
When we spoke with Hamilton about the program in December, he estimated that a four-person research team could cover around 10 hectares per day, and the drone AI method allows the team to cover 50 hectares in two. hours.
Over the past few weeks, a number of high-profile cyber attacks have impacted critical U.S. infrastructure, ranging from oil production and meat manufacturing to local water supplies. As we reported earlier this year, a number of teams overseeing network security at water treatment facilities are using AI-enabled systems to provide 24-hour monitoring and response. short-staffed IT teams.
But the increasingly common use of “artificial intelligence” sometimes requires a more precise semantic conversation. After all, are these apps really AI or are these solutions more akin to pattern matching?
âFrom my perspective, artificial intelligence is a general term that refers to software with a set of specific goals. In general, most of the current techniques used by security teams are best defined as machine learning algorithms, âsaid Peleus Uhley, chief security officer. strategist and senior scientist at Adobe. “Our team frequently uses machine learning algorithms to solve a variety of IT security issues, such as anomaly detection.”
Using machine learning to detect anomalies is “markedly different from pattern matching,” Uhley said, adding that “you know in advance what you would consider an anomaly” when using pattern matching; which means that teams “should have a predefined, fixed set of models that you match for a given environment.”
However, machine learning algorithms allow teams to “take a more generalized approach” and apply the same algorithm in a number of environments, he added.
“A machine learning algorithm is ‘taught’ what is ‘normal’ for each given environment and can then identify anomalies from that baseline. This can often produce better results than pattern matching because the ML algorithm is not limited to a finite set of pre-defined rules, âUhley said.
âIt may be able to detect things that are outside the scope of pattern matching,â he continued.
Earlier this month, Kaspersky released a report on the use of computer algorithms in dating apps and feelings about the role these algorithms play in modern matchmaking. Overall, 44% of respondents “would trust AI or an algorithm to find them a compatible match” and a similar number (43%) prefer “to only see people who have been determined to be a good match by algorithm, âaccording to Kaspersky.
Conversely, more than a third of respondents (39%) said that they “find it dehumanizing to be sorted by an algorithm”, 58% would prefer “to have equal access to everyone on an application” rather than having a “people sorting algorithm for them,” and more than half (56%) don’t think algorithms “can really capture the complexity needed to understand attraction,” according to the report.
Algorithms are also implemented to add a layer of security to dating applications. Kaspersky security expert Vladislav Tushkanov and security researcher David Jacoby said machine learning algorithms can help identify bots, potentially identify cases of grooming as well as cat fishing, and use language processing natural to detect “abusive language or inappropriate messages, such as spam or promotional texts.”
Computer vision, on the other hand, can automatically filter out unwanted sexual images (unless the user actually wants to engage in sexting). Finally, algorithms can be applied to analyze user behavior in order to block them. fraudulent accounts, “Kaspersky representatives explained.
While many apps will tout AI capabilities with their latest products and services, questions remain as to the accuracy of some claims; namely, is it really artificial intelligence, model matching or smart marketing?
âI imagine a lot of the uses of technology to match would fit the definitions of AI that we use. I’m sure some of the matches work from simple heuristics – you’re a match with someone in your zip code if there aren’t other people to relate to, for example, âsaid Whit Andrews, senior analyst at Gartner.
“I’m sure others are more sophisticated, using much richer analyzes that draw n-dimensional polygons that define a given person, or behavioral matches that snap to variables, even if you’re online. same time, âhe said. .
To sum up these points, Andrews said he’s “sure” that businesses “are using AI, but a lot of people would say pattern matching is AI. I’m not sure they’re still using AI. probabilistic analysis, but I’m sure they sometimes do. “