The information age has reached a turning point. See how AI is being used to solve the world’s problems and register for our upcoming webinar to learn more.
The information age has reached a turning point, with data constantly being produced by devices, and sensors as the Internet of Things continues to grow. Yet, unless we interpret it, that data remains just what it is – data. In order to turn this data into actionable insights, we need the help of artificial intelligence and its problem-solving abilities.
Whereas humans have the potential to make a lot of errors, AI has the benefit of being supported by machines, which have much superior computation abilities than humans, such as the ability to sift through enormous amounts of data and use that data to make better decisions. Simply put, AI has the ability to do the heavy thinking for us. What exactly does that heavy thinking comprise of?
In addition to AI, machine learning (ML) is a technological subset of AI that allows computers to adjust when exposed to new data – basically, learning without being explicitly programmed. ML is equivocal to data mining in that databases are examined by humans to produce new insight and information. Yet, ML is able to provide an unbiased analysis of the data.
Each industry features a combination of interconnecting inputs and variables. Analyzing complex data to understand meaningful value can often overwhelm human analysts, keeping us from finding adequate solutions in a timely manner.
Many tech companies are in the midst of developing AI solutions, allowing the companies to:
AI will eventually be found in every industry on the planet. Here are a few industries AI will transform:
AI can be applied to cybersecurity in a preventative and predictable way. For instance, AI prediction technology can be used to study millions of files and attacks to understand what exactly makes them up. By comprehending mathematical DNA, companies can prevent future attacks.
In the energy sector, companies can utilize AI to sort through vast datasets to predict and adapt to specific scenarios. There are several ways they can reduce operational costs and proactively mitigate issues, such as increasing automation, decreasing downtime, optimizing asset management, identifying efficiencies, and improving operational performance.
Essentially, anything that’s data-driven – like analyzing MRI scans and detecting early forms of disease or cancer – can benefit hugely from machine learning. One of the biggest benefits of AI is its ability to sift through massive amounts of data in record time, helping researchers pinpoint areas of focus for their own research. For instance, a recent discovery on Amyotrophic Lateral Sclerosis (ALS), was discovered thanks to a partnership between Barrow Neurological Institute and the artificial intelligence company IBM Watson Health.
The artificial intelligence computer (known as IBM Watson) reviewed thousands of research pieces to identify new genes that are linked with ALS. Another promising use of AI when it comes to healthcare is its ability to predict the outcome of drug treatments. For example, cancer patients are often given the same drug, then monitored to see how effective that drug is. Using AI, scientists could predict which patients benefit from using a particular drug with data, saving time, money, and providing a highly customized approach.
On the patient side of things, an AI-driven healthcare system could lessen some of the burdens on a system that is struggling to keep up with ever-growing and evolving demand. Being able to access this technology will allow patients to make better health decisions, diagnose disease and other health risks earlier, avoid expensive procedures, and ultimately, live longer.
From a Google search to self-driving cars, AI essentially encompasses everything. For instance, well-known streaming platform Netflix gives users what they want by collecting a vast amount of consumer data, letting them know what shows you watch, when you watch them, when you pause, rewind, and more, seeing everything in real time.
An example of the decisions Netflix makes based on the big data from their worldwide 30 million subscribers includes their programming. After running the numbers through their AI technology, they determined people like the British version of House of Cards, David Fincher movies, and films featuring Kevin Spacey. They made a decision based on this information, buying House of Cards as a result.
AI technology is being used to look at financial model to garner greater levels of predicting future pricing patterns, achieving greater levels of trend analysis, identifying new markets, and assessing supply chain risks.
Synthesizing and disseminating inputs rapidly can help alert governments to make better decisions on crucial social issues, the environment and economy, all in real time. They can place sensors on everything from mountains to streetlights, and by applying AI to that data, governments can accomplish things like building more livable cities, preventing crime and terrorist attacks, reducing poverty, and understanding climate change.
Another area that can use big data to gain insights into conflicts before they occur is the military. AI programs can utilize satellite photo interpretation capabilities in order to identify potential targets and threats. Further, by analyzing speech patterns in communications, AI can look for certain phrases and words that may point to terrorist activity, then respond efficiently to lessen the situation before it escalates.
Procurement departments have been using spend analytics software to utilize big data to the fullest. AI software could help the procurement industry overcome huge challenges, such as risk analysis of suppliers, monitoring exchange rates, comparing prices of suppliers, managing supply chain risks, and finding the best value without compromising quality. Some of the benefits of AI is wrapped up in the fact that companies could garner huge savings if buying decisions are accelerated.
Clearly, no matter the industry, the potential for AI is great. Rule-based analysis and machine learning can find ways to create efficiency, reduce costs, and optimize working environments. As more and more parts of our lives and work generate huge amounts of data, it stands to reason that eventually, AI will contextualize the data and extract meaningful insights, allowing companies to make more informed decisions and improving their bottom line.
Machine learning and AI also have the potential to extend outside industries, providing help that can strengthen industries and economies overall. See the following examples from SciPol:
Though self-driving cars are still a few years away from being fully safe to drive, this area of AI could dramatically decrease the rates of deaths and injuries on the roads. According to a report drafted by Stanford University, self-driving cars have the ability to reduce traffic-related deaths and injuries. The change will also bring about shifts in our lifestyle, with passengers using the time freed up from driving to giving us more time to get work done on our commutes or entertain ourselves. We also may have more of a choice where we work from thanks to self-driving cars, with the study reporting that the increased comfort and decreased cognitive load may affect where people choose to live.
In 2016, the Georgia Tech News Center reported that an artificial intelligence course created an AI teaching assistant. At the end of the course, students enrolled in Georgia Tech’s online master’s of science in computer science program found out that their teaching assistant was in fact a virtual assistant. And her work was necessary – with roughly 300 students enrolled and posting 10,000 messages in the online forums, it was too much for the professor and his eight human TAs to handle.
After a few initial hiccups, the robot started answering the students’ questions with 97% certainty. The robot was designed by the university after their research revealed that one of the main factors behind students dropping out was a lack of support. The use of this type of robot is revolutionary for universities. People learn differently, at different starting points and at different speeds. Thanks to AI, students could learn in a more personalized way. And since most education systems can’t afford to tutor every child, AI can be deployed in a useful way.
AI could help us be more efficient with our energy consumption; and in some parts of the world, this is already happening. Tech giant Google has an enormous data center that requires a massive amount of energy to run the servers and keep them cool. To combat this effect, Google has deployed its AI platform Deep Mind to predict when its data centers will get too hot. As a result, cooling systems are only activated when required, saving Google around forty percent at its server farms.
Wildlife transformation can be transformed thanks to the analysis of massive amounts of data. An example of this comes with tracking animal movements, which allows researchers to see where they go and as a result, which habits need to be protected. For instance, this Montana-based study pinpoints the best places to create wildlife corridors - continuous areas of protected land that link zones of biological significance that animals can use to move safely through the wilderness - for wolverines and grizzly bears.
At Stefanini, we deploy AI-based solutions every day and look to AI and ML to lead us into the future. In fact, we are in the midst of nearshoring talent in LATAM to leverage already-existing talent that is often overlooked.
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