Five Machine Learning Algorithms Entrepreneurs Should Understand
Machine Learning – Entrepreneurs and investors should take note of how artificial intelligence (AI) is disrupting business in every vertical.
As an entrepreneur myself, and the founder of an AI news website, I’ve had a front-row seat to the disruption, and I’d argue that much of it has been achieved by a subfield of AI called machine learning. We define machine learning (ML) as “a method of enabling computers to carry out specific tasks without explicitly coding every line of the algorithms used to accomplish those tasks.”
There are five fields of machine learning that you should be aware of. While some of these may be familiar, others are less commonly known.
This type of machine learning started as far back as 1943. While the technology initially showed promise, it had to endure several AI winters before it re-experienced a surge in interest in the early 2000s. The underlying fundamentals have not changed; what has changed is the rapid exponential growth of computer technology brought about by Moore’s Law, which states that the speed and capability of computers can be expected to double every two years.
Deep learning is what is used to feed data into an AI system and is most often most closely associated with data science. Using pattern recognition, the AI can identify patterns in data. This can be used for facial recognition, computer vision or any type of object detection. Futuristic technologies, such as autonomous vehicles, will be reliant on this technology.
Reinforcement learning has been successfully used to challenge humans at various types of games, such as chess and Go. Reinforcement learning involves the AI attempting to increase the reward it receives when interacting with a complex, uncertain environment. The reward can take the form of points, or any reward programmed in by the developer.
Reinforcement learning learns by playing against itself in a simulated setting. The AI improves on itself after every iteration until, eventually, it is superior to any human player. This is what was used to defeat Go champion Lee Sedol in 2016.
It will be difficult to find an AI system in the future that is not taking advantage of reinforcement learning. In a business setting, a system can perform A-to-B comparative analysis and continue to optimize a system indefinitely. Instead of the reward being points, it could be something basic, such as increasing click-through rates on ads or successfully diagnosing instances of cancer.
Deep Reinforcement Learning
Deep reinforcement learning is a complex system that uses both deep learning and reinforcement learning. The benefit of using both in the same AI system is that there’s a direct feedback loop between the two. A savvy entrepreneur feeds data to the AI, and using deep learning, the AI recognizes patterns in the data. Reinforcement learning then enables the system to optimize how patterns in the data are recognized by teaching itself how to do more with the aggregated data.
For example, the deep learning algorithm may use reinforcement learning to optimize the same dataset in two different ways. The technique that achieves the highest score (such as recognizing the most images) is then the more successful of the two. Using reinforcement learning, the AI can reoptimize the same dataset over and over, and continuously choose the most successful approach.
This means, regardless of the business you are in, deep reinforcement learning can improve workflow and increase productivity.
So far, we have used data science to collect information that has been fed into a deep learning or deep reinforcement learning system. The problem for many is the actual data collection. I’ve found that this is a serious issue in certain industries, such as health care.
Federated learning, according to our definition, “links together multiple computational devices into a decentralized system that allows the individual devices that collect data to assist in training the model.” The principal benefit is that no single entity controls the data. This enables hospitals to share data without worrying about privacy issues. Privacy issues can be further remedied by having data that is identifiable wiped clean before it is distributed. The AI system will never know who the data belongs to.
This opens up opportunities for locating biomarkers, which can be used to correctly predict relevant clinical outcomes. The more data that is fed into the deep learning system, the more patterns in the data can be recognized.
Federated learning can enable an exponential acceleration in data collection, which can be used to fight cancer, Covid-19 or other infectious agents. It can also be used in other data-hungry verticals, such as defense and insurance.
This is a less popular type of machine learning algorithm, but in many ways, it is both the easiest to understand and the most powerful. Meta-learning is an algorithm that essentially learns how to learn. It can also learn with less. Meaning, if you have a limited quantity of data, meta-learning can be valuable.
What makes this type of AI important is that it can generalize to many different scenarios. How it achieves this is by separating a specified task into two functions.
The first function is a quick acquisition of knowledge within a specific task. The second and most important function involves the extraction of information learned from all previous tasks.
With this secondary function, the AI can use generalized learning when new tasks are found. This is similar to how a human behaves. A human would approach a unique problem by using knowledge gained from previous unrelated experiences.
What meta-learning offers is an optimized AI that can be used on new tasks of which the system has no previous training. This enables an entrepreneur to penetrate completely unrelated industries using unrelated datasets.
The above machine learning technologies have witnessed incredible growth. Progress in AI is advancing quickly, and entrepreneurs and investors who best understand this technology will be best positioned to take advantage of new opportunities.
This article originally appeared on forbes.com To read the full article and see the images, click here.
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