What is Artificial Intelligence?
Artificial intelligence (AI) has been portrayed in movies for years as runaway computers in big buildings with lots of servers locked away in a white room. In reality, artificial intelligence is a far more mundane, but just as powerful reality that is becoming more and more commonplace in today’s business world.
AI has been around as an idea for years. In fact, the first official paper written on the matter was published in 1956. Looking back more than 50 years later, many of the ideas were spot-on with capabilities and capacity.
Believe it or not, most of us are already interfacing with AI; we just don’t know it explicitly. Every time we search for something, exchange information, fill out a survey or account form, or anything that is collecting information from a user, we are likely interacting with AI already.
The Goals of Artificial Intelligence
Originally, everyone thought the main benefit of AI would be to replace human function and workers, making processes far more efficient with its speed and capability. Today, the goals of AI are far loftier- as seen below.
Reasoning, Problem Solving
An easy example of AI problem solving comes in the form of statistical probability. A number of models are being used to determine what could happen with future possibilities where lots of factors are involved. This is a commonly used approach in health and disease transmission, economics, and insurance risk management.
AI is particularly helpful in resource planning on a large scale for communities. Models built can anticipate and show what could happen when lots of people build a community and grow over time.
College testing and classes have been using simplistic AI tools for a while now, increasing adaptive testing that gets more challenging as a student is successful. This is a far more assessment-accurate method versus the traditional flat score testing that has been used for decades.
Natural Language Processing
Have you recently dealt with a
The Turing Test of Computer Intelligence
In 1951, Alan Turing introduced the idea of “The Imitation Game.” The original test had three rooms. The first room had a judge. The second room had a man, and the third room had a woman pretending to be a man. The judge could only see their communication, but not the people themselves. The judge then had to decide who the man was based on their communication. This initial idea of a test was then modified to include a computer to test the believability of a computer over a human. The judge would then choose which room contained a computer, and which was a human, depending on their verbal queues. According to Turing’s conclusion, if the machine won more than 50 percent of the time at fooling the judge, it must be intelligent. This is a bit of a leap of logic, but the concept is still the standard AI tries to reach today.
The Difference Between AI and Machine Learning
Machine learning today works on the foundation of the neural network. This involves a computer infrastructure that doesn’t just store and follow commands. It tries to calculate conclusions independently, based on the information it receives- essentially a rudimentary mirror of how a human brain works. However, because a machine doesn’t have the spark that a live brain does to creatively wonder and reach conclusions, it does something similar using statistics and probability instead. Unlike a regular computer, however, the machine learning refines its probability conclusions with each result, becoming more accurate with each turn, as it builds its own calculated data on “experience.”
AI involves devices designed to think and make decisions on their own. This goes beyond just running an algorithm based on probability, and classifying new results similar to past results. Instead, AI is the general breadth of computers acting on its own volition (which includes machine learning as one of a number of tools the computer can use to make things happen). However, AI continues to be driven by the basic concept that it must first be told what to do, which requires a human. The challenge is to get beyond being “coded” what to do.
The Scope of Artificial Intelligence - Where is it Going?
Artificial Intelligence Vs. Human Brain
The K Supercomputer Simulation
Emulating a brain is not easy. To date, the latest attempt involved a Japanese supercomputer, the K Supercomputer, that took about 40 minutes to crunch the amount of data the average human brain processes in one second. Yes, that’s correct – it takes one of the world’s biggest computers almost an hour to do what a brain does in a blink of an eye. In that
However, it is not to said that computers are dumb. In fact, they can easily fool each other with the right coding. This was proven by a YouTube video of Matt Unsworth’s creation. A computer and robot arm visually showed it could dupe the typical anti-bot tool, CAPTCHA, and it was a real human entering the data. After a few clunky moves, and succeeding admirably, the video ends with the arm dropping the digital pen in
Exascale Computer to Beat Human Brain Processing Speed
Will there ever be a computer capable of AI capacity? Some think an exascale computer will be that answer, but it doesn’t exist yet. China is trying to build one with almost 83,000 processors involved. If operational, the Chinese system would be capable of 1 quintillion calculations. But it probably still couldn’t figure out how to squeeze toothpaste from out of a tube properly. While we jest, that’s the cold hard truth- machines can’t think like humans so far.
Transition Periods: AI to Beat Human Errors
How Does AI Minimize Human Error
AI doesn’t correct human error or mistakes automatically. It goes through the same logical process of figuring out which alternatives work, and which ones don't. By a process of elimination, AI then gets to the correct result. However, the difference between AI and a human is speed; AI works faster at arriving at the correct answer. If speed is the difference that matters (for example, timing stock trades), then it provides a big advantage over a human competitor.
Business Applications of Artificial Intelligence
AI Combined With Big Data Means A Smarter Business Intelligence
One of the most immediate applications of AI is screening of candidates for hiring, used by human resources fields. Some tools are already available on the market, and are excellent choices for finding the right candidates for a job based on skill set. The data is then converted into the next sequence of interviews, complete with candidate portfolio packages for quick review.
Both marketing and strategic planning are clearly taking advantage of big data mining, allowing AI to quickly find the nuggets that would take months for even experts to discover. These are often based on behavioral data that occur over time, ergo lots of number crunching and statistics. AI goes beyond just alarm triggers when certain conditions occur; it looks for abnormalities.
AI is also making a big presence in logistics. The problem with sourcing resources is that most supply systems don’t match changing conditions. An AI system can break the paradigm of the locked-in contract with a mismatched vendor, to a need. The AI system works on need-defined ordering, finding, and pulling what supply sources are needed, correctly.
With AI, the customer support calls are routed to the exact person or resource that has the answers they need. If people can get past giving input to a system, they often find that the response actually works better from an AI system.
The Growth Tool Small Businesses Need
AI isn’t the Holy Grail of technology, but it is a game-changer for business, regardless of size. AI, with all of its possibilities, is a major disruptor of current markets- constantly opening new avenues, and allowing smaller players to grow.