Many individuals often use the terms artificial intelligence (AI) and machine learning interchangeably, but they are separate entities. Machine learning falls under the umbrella of AI. AI is the general concept of machines performing functions in a smart way. For example, in the early days of AI, engineers built logical machines that could perform complex calculations. Now, people are more interested in machines performing tasks as humans do. This is where machine learning comes in. Machine learning is when a computer can access information to learn and improve on its own without a programmer doing the work. This allows computers to learn by themselves.
How Machines Learn
Experts categorize machine learning as supervised and unsupervised. However, there are several subsets to these two main classifications.
- Supervised machine learning occurs when machines take data from past events and use it to predict future events. In essence, a programmer gives the machine the answer and allows it to extrapolate outputs for future similar events. Given enough time, the machine will be able to provide solutions for any new inputs. It can also compare its own output with the output the developer intended. It can then identify any errors with its process and adjust accordingly.
- Unsupervised machine learning occurs when developers give machines unclassified data rather than set outputs. The machine uses the information to infer the sought after function.
- Semi-supervised machine learning, as the name implies, falls somewhere between supervised and unsupervised learning. Developers give the machine classified and unclassified information. In most semi-supervised settings, the machine works with mostly unclassified and some classified information. Machines that learn in this setting improve their accuracy at a rapid pace.
- Reinforcement machine learning occurs when a machine interacts with its environment to discover errors or rewards through trial and error. The machine is able to infer the desired performance and make the most of its abilities.
Facts About Machine Learning
AI is a buzzword that can mean any number of things depending on who you ask. Because of this, there is rampant confusion behind what machine learning really is. Below are several facts about machine learning to help clarify the subject.
- Machine learning requires algorithms and data, but the data is more important. The complexity of the algorithm does not affect whether a machine can learn or not. However, the quality of data can. Machines are limited to the data presented to them.
- Machine learning requires a great deal of data. The more complex the model, the more data the machine will need. Machines learn from patterns in data. If the parameters are too large and your data insufficient, the machine will have poor predictive performance. This is known as overfitting. Once this occurs, the machine will overreact to small fluctuations in data resulting in several errors.
- If you have not yet noticed, machine learning is dependent upon data. It should come as no surprise that the quality of the machine’s learning is limited by the caliber of data used to train it. For example, in a supervised setting, machines need a hearty assembly of data rich in details and accurate labeling. Low-grade data will produce low-grade learning.
- The risk of user error is high in machine learning. More often than not, a failed system is the result of the human operator rather than the machine. For example, a supervised machine may fail to learn because the provided data contained human biases.
- The machines are not going to rebel and destroy us all. Most of what the public knows about AI comes from movies. However, directors design these movies to excite, thrill, or terrify. They want to evoke an emotion or reaction. Creating a doomsday AI scenario fits the bill nicely, however unrealistic. In order for such an event to happen, machine learning would need to progress leaps and bounds. Even then, humans would have to give machines the ability to revolt. The idea of an individual building a robot that can decide to kill its creator on a whim is an unlikely one.
While detractors may spin science fiction end of the world scenarios involving AI, there are some clear dangers associated with the technology. Bias in particular poses the greatest risk. For example, when a scientist develops an algorithm, his or her bias may work its way into the system. The machine will learn these biases and reinforce them rather than recognize them as opinion. This is because it is a machine; it takes presented data as fact.
Such biases have significant real-world effects. For example, algorithms for determining credit scores can do lasting damage to a multitude of people if the developer unwittingly applied their personal biases to the machine’s algorithm. While it is difficult to construct fairness into algorithms, it is not impossible. As the saying goes, with great power comes great responsibility. It is a scientist duty to create fair and unbiased algorithms.
Why Do We Need Machine Learning at All?
Given the potential for algorithm biases, you may be wondering what the point of machine learning is. History shows us advancement is inevitable. The industrial revolution led to the rise of manufacturing. The second industrial revolution (also known as the technological revolution) led to rapid industrial developments such as railroads, use of machinery in manufacturing, electrification, and more. The human thirst for knowledge and advancement is an unstoppable force.
It also provides solutions to many real-world problems. One of the greatest areas where machine learning can thrive is in financial security and preventing fraud. Machines can protect companies by mining data to identifying high-risk accounts as well as warning signs of fraudulent behavior. This is just one of several implications for machine learning. Machine learning can improve health care services, reduce government inefficiencies, and leverage purchasing behavior to improve marketing campaigns. To learn more about machine learning, contact the experts at Bright Apps.