Artificial Intelligence, or AI, can vary from as simple as your ice maker knowing to make more ice to very complex algorithms used to detect national threats. The term AI is a blanket statement that is typically used to describe any situation where a machine is choosing a task based on an input that matches previously interpreted data.
AI accounts for many subsets of applications, including machine learning among many others. In this article, we look at artificial intelligence, machine learning, and the technologies that support these monumental computations. We will also look at how AI is used in an everyday business sense to help organizations reach their goals.
What is Artificial Intelligence?
AI is really any device or system that can assess data external to itself and learn from that data and accomplish its goals. One of the simplest ways to envision this would be the example of modern thermostats. The thermostat can be trained to react with different settings based on time of day or when people leave the room or come back. So far, this is AI at one of its most basic forms.
According to Wikipedia, artificial intelligence is “Artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals.”
However, according to Bill Gates, “Humans should be worried about the threat posed by artificial intelligence.”
The true answer most likely lies somewhere in between…
Historical training and inference
AI is not a new concept. In 1959, Arthur Samuel thought that instead of teaching a computer what it should know, we should feed it information and let the computer make its own decisions. Fast forward to now, we live in a time where almost everything is stored or documented on the internet.
In some cases, engineers found it more efficient to program a system to think like a human and plug them into the internet to learn. Many readers of this article may remember absurd movie scripts that were written by AI. It’s not perfect, but it is a start.
Let’s talk machine learning
As industrial as it sounds, machine learning is quite simple. Users or machine input data to reach the desired outcome. The more data fed into the machine, the more accurate the output becomes. However, there are several approaches to machine learning:
– The computer is given an established data set and their desired outputs.
– The goal is to learn a general rule that maps inputs to outputs.
– Leaving the computer to go on its own to find structure in its input.
– The goal of this approach is to discover hidden patterns in data.
– The computer program works with an ever-changing environment to perform a task.
– As it makes its way through the environment, the program is constantly fed new data and works toward optimizing its own solution.
– This is the new dominant application in machine learning today in big data.
– This is especially useful when you’re trying to learn patterns from unstructured data.
Making sense of the data
As the world continues to grow at an alarming pace, we need to keep up. Data today is exploding in leaps and bounds. Experts estimate that internet users create 2.5 quintillion bytes each day. Remember that much of this data is unstructured and machine learning is a great way to parse through the data and extrapolate what is most useful.
To make sense of unstructured or machine-generated data, administrators need an impressive amount of compute. Most often, Graphics Processing Units or GPUs offer this level of high-performance. GPU servers have the ability to run multiple processes at the same time, referred to as parallel computing. Each thread or computation may be slower, but the ability to compute up to 100 threads at a time makes short work of processing large amounts of data.
Current big data use cases
Many SMEs feel that AI and machine learning are great, but offer no value to their scrappy existences. However, this is not the case. While your small business may be in the cost-cutting phases of startups, there are many ways that these transformative technologies can boost your bottom line.
Ecommerce businesses use AI to recommend similar products, help gain insight into customer habits, and customize the sales funnel.
AI can help small businesses search out potential threats based on user data, activity, and routines.
Many SMEs adopt third-party tools to help gather customer feedback and provide services during off-peak hours.
Automating the hiring process allows businesses to streamline their onboarding processes to hire faster and more efficiently.
How to adopt AI into your business strategy
1. Set a goal and a timeframe
What is it that you are trying to achieve? Be very specific about your expectations when adopting AI applications and practices.
2. Measure benchmarks
Set timeframes to measure success along the way and adjust your strategy accordingly.
3. Determine success
Once you’ve reached the end of your preset trial period, determine whether or not you’ve reached your preset goals. If so, look for ways to permanently install AI into your day-to-day business activities. If not, try to determine why your machine learning attempts failed and try again.
Your intelligent future
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