AI vs. Traditional Software

Artificial intelligence has come to represent the broad category of methodologies that teach a computer to perform tasks as an “intelligent” person would. This includes, among others, neural networks or the “networks of hardware and software that approximate the web of neurons in the human brain” (Wired); machine learning, which is a technique for teaching machines to learn; and deep learning, which helps machines learn to go deeper into data to recognize patterns, etc.

Within AI, machine learning includes algorithms that are developed to tell a computer how to respond to something by example. Deep learning is a type of machine learning that uses a structure as close as possible to the human brain—neural networks—as a model for learning.

MIT Technology Review says: “Deep-learning software attempts to mimic the activity in layers of neurons in the neocortex, the wrinkly 80 percent of the brain where thinking occurs. The software learns, in a very real sense, to recognize patterns in digital representations of sounds, images, and other data.”

“On the other hand, traditional programming is a manual process—meaning a person (programmer) creates the program. But without anyone programming the logic, one must manually formulate or code rules. In machine learning (AI), on the other hand, the algorithm automatically formulates the rules from the data”. Notice the difference between traditional programming and AI?

Traditional programming tends to be influenced by human biases. Humans tend to be influenced by their educational background and biases toward topics or other humans. Additionally, which would you say is more scalable with the highest quality outcomes, the least variations in conclusive directions as well as analysis? The answer should be obvious.

Follow the Money

According to the Brookings Institute “AI companies attracted nearly $40 billion globally in disclosed investment in 2019 alone. American companies attracted the lion’s share of that investment: $25.2 billion in disclosed value (64% of the global total) across 1,412 transactions. (These disclosed totals significantly understate U.S. and global investment, since many deals and deal values are undisclosed, so total transaction values were probably much higher.)”

Re-engineering Old Software Using AI

In August, the young artificial intelligence process automation company Intelenz, Inc. announced its first U.S. patent, an AI-enabled software-as-a-service application for automating repetitive activities, improving process execution, and reducing operating costs. Consider the data that flows from SAAS technology and you’ll understand how powerful that data becomes by embedding machine learning into SAAS.

Another example of enabling the power of AI to improve performance and user engagement with old software solutions is Absorb, an LMS powered by Machine Learning.

The artificial intelligence in the Absorb platform enables your platform to automatically perform some of the actions that you would do manually yourself, either as a learner or an Admin, such as tagging training materials included in courses and the social learning assets that you contribute to the Discover, Coach & Share module, or suggesting which courses are most relevant for learners.

By leveraging the power of machine learning, Smart RankingTM is an Absorb feature that provides the best possible search results—and it comes standard for all Absorb users. As users search for terms, the algorithm learns from what they select to present better results to future users.

There are dozens of other examples of traditional software becoming empowered by AI/Machine Learning. It is only a matter of time before all software will be enabled to become “smart software” powered by AI. It would be wise for Developers to get ahead of the curve by becoming proficient in AI or they will be run over by those that do.

What say you?