Posted on April 12, 2021 · Posted in Blog, General, Memo Plus Gold, Personal

Artificial intelligence is a broad and general term that refers to any type of computer software that engages in humanlike activities, including learning, planning and problem-solving.

Machine learning

Machine learning is one of the most common types of artificial intelligence in development for business purposes today. Machine learning is primarily used to process large amounts of data quickly. These types of artificial intelligence are algorithms that appear to “learn” over time, getting better at what they do the more often they do it. Feed a machine learning algorithm more data and its modeling should improve. Machine learning is useful for putting vast troves of data – increasingly captured by connected devices and the internet of things – into a digestible context for humans.

For example, if you manage a manufacturing plant, your machinery is likely hooked up to the network. Connected devices feed a constant stream of data about functionality, production and more to a central location. Unfortunately, it is too much data for a human to ever sift through, and even if they could, they would likely miss most of the patterns. Machine learning can rapidly analyze the data as it comes in, identifying patterns and anomalies. If a machine in the manufacturing plant is working at a reduced capacity, a machine learning algorithm can catch it and notify decision-makers that it is time to dispatch a preventive maintenance team.

But machine learning is also a relatively broad category. The development of artificial neural networks, an interconnected web of artificial intelligence “nodes,” has given rise to what is known as “deep learning.”

Deep learning

Deep learning is an even more specific version of machine learning that relies on neural networks to engage in nonlinear reasoning. Deep learning is critical to performing more advanced functions, such as fraud detection. It can do this by analyzing a wide range of factors at once. For example, for self-driving cars to work, several factors must be identified, analyzed and responded to all at once. Deep learning algorithms are used to help self-driving cars contextualize information picked up by their sensors, like the distance of other objects, the speed at which they are moving and a prediction of where they will be in 5-10 seconds. All this information is calculated side by side to help a self-driving car make decisions like when to change lanes.

Deep learning has a great deal of promise in business and is likely to be more commonly used soon. Older machine learning algorithms tend to plateau in their capability once a certain amount of data has been captured, but deep learning models continue to improve their performance as more data is received. This makes deep learning models far more scalable and detailed; you could even say deep learning models are far more independent.

Artificial intelligence and business today

Rather than serving as a replacement for human intelligence and ingenuity, artificial intelligence is generally seen as a supporting tool. Although artificial intelligence currently has a difficult time completing commonsense tasks in the real world, it is adept at processing and analyzing troves of data far more quickly than a human brain could. Artificial intelligence software can then return with synthesized courses of action and present them to the human user. In this way, humans can use artificial intelligence to help game out possible consequences of each action and streamline the decision-making process.

Artificial intelligence is a form of software that makes decisions on its own, that is able to act even in situations not foreseen by the programmers. Artificial intelligence has a wider latitude of decision-making ability as opposed to traditional software.

Those traits make artificial intelligence highly valuable throughout many industries, whether it is simply helping visitors and staff make their way around a corporate campus efficiently or performing a task as complex as monitoring a wind turbine to predict when it will need repairs.

Machine learning is used often in systems that capture vast amounts of data. For example, smart energy management systems collect data from sensors affixed to various assets. The troves of data are then contextualized by machine learning algorithms and delivered to human decision-makers to better understand energy usage and maintenance demands.

Artificial intelligence is even an indispensable ally when it comes to looking for holes in computer network defenses. We really cannot have enough cybersecurity experts to look at these problems because of scale and increasing complexity. Artificial intelligence is playing an increasing role here as well.

Artificial intelligence is also changing customer relationship management (CRM) systems. Software like Salesforce or Zoho requires heavy human intervention to remain up to date and accurate. But when you apply artificial intelligence to these platforms, a normal CRM system transforms into a self-updating, auto-correcting system that stays on top of your relationship management for you.

Another example of artificial intelligence’s versatility is within the financial sector. Banks are integrating artificial intelligence into regular banking operations, such as mortgage loans. Using this technology, if you have a mortgage with the bank and it is up for renewal in 90 days or less … if you are walking by a branch, you get a personalized message inviting you to go to the branch and renew the purchase. If you are looking at a property for sale and you spend more than 10 minutes there, it will send you a possible mortgage offer.

The users are no longer expected to constantly be on a search box ‘googling’ what they need. The paradigm is shifting as to how the right information finds the right user at the right time.

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