The field of Artificial Intelligence consists of different cutting-edge technologies like machine learning, artificial neural networks, natural language processing, and many more. Depending on how these tools are applied, they can range from very simple to extremely complex in architecture and implementation.

In a business context, AI is generally understood to be the driving force behind analytics that provide a proper understanding of statistics and business models and suggest practical steps for improvement. In the consumer sector, the involvement of such technologies in our daily lives is inevitable, and we use it with or without our knowledge. Some industries where these technologies are bringing change are finance, e-commerce, manufacturing, healthcare, engineering, and many more.

Artificial intelligence, artificial learning, and machine learning are often used as interchangeable terms, especially with the rise of big data. However, these terms refer to different technologies, and it is essential to understand how they can be applied.

Understanding the Terms

Artificial Intelligence: When a machine shows intelligence properties similar to a human, it is called artificial intelligence. AI allows machines to mimic human behavior. In fact, some AI-powered computers possess the ability to simulate human brain-like characteristics including sensation, actions, perspective, and cognitive abilities. An example of artificial intelligence in our daily life is the use of any personal assistant like Google Now from Google, Cortana from Windows, and Siri from Apple.

Machine Learning: Machine learning is a subset of AI and focuses on machines' ability to receive a set of data, learn for themselves, and change their algorithms as they learn more about the information that they are processing. An example of machine learning is the use of any speech recognition software on tools like Google text-to-speech or Google voice typing.

Data Science: Data science is similar to machine learning in that algorithms designed to receive inputs analyze their output values statistically for the purposes of prediction.

All the terms mentioned above are correlated. Several technology products feature these buzzwords prominently, so it's important to understand precisely what they refer to.

Trying to define terms like AI, ML, or Data Science without a proper knowledge of each subject can create huge understanding gaps. Every buzzword has its meaning and context.

AI, in particular, is a wide-reaching field that also includes certain aspects like understanding multiple languages, planning, pattern recognition, problem-solving abilities, and sound recognition. Considering the industry is flooded with terminologies like these, choosing the right algorithms can make a large difference when building a product.

Artificial Intelligence Explained

AI is an increasingly used term in today's technological world. In this context, AI refers to machines that can copy human intelligence properties like learning and problem-solving. As a branch of computer science, AI refers to the process of simulating human mind operations with machines. Natural intelligence plays an essential role in this operation.

AI relies on the assumption that human intelligence can be accurately described and understood, so that machines can reproduce it with AI. For this reason, most AI computers are constructed in a way that imitates or mimics the human mind, and then fed large quantities of data to "train" them.

A good example is Google Now, which takes time to learn about your daily habits, routes, essential texts, and calls, and makes suggestions accordingly. Though its default setting performs at an average level, Google Now does an extraordinary job of being your assistant once it learns about your habits and processes.

After its learning phase, business AI can identify new sources of revenue, reinvent existing business strategies, and strengthen the ability of human employees to drive growth.

In this sense, the real value of AI lies in the synthesis of humans and machines rather than the machines themselves. Many technological and non-technological companies are beginning to invest in the possibilities and applications of AI for this reason. In the most common use case, AI has a sophisticated pattern recognition capability that helps businesses understand the market for better investment opportunities. In other cases, AI is on the user-end, making communication between businesses and consumers easier.

A Basic Illustration of AI's Capabilities

Consider the following basic principles of modern AI:

1. When calculating simple mathematical problems, any computer is capable of beating a human at speed and accuracy.

2. While identifying an animal in a given picture, a human can do it with higher accuracy and speed than a machine.

In case 2, AI does not need to demystify anything, so its mathematical abilities are outperformed by the sensory organs of the human body. When the problem space involves structured data or concrete symbols, technologies close to natural intelligence and the computational power of cognitive systems are a reality.

Machine Learning Explained

Artificial intelligence and machine learning seem like similar terms, but they are not the same. Machine learning is a method by which a machine attempts to achieve artificial intelligence. In other words, it is a subset of AI.

Practical applications for machine learning are as follows:

  • Big Data – Machine learning is optimal for combing through, organizing, and visualizing the massive quantities of organizational, user, and market data that businesses now gather.
  • Business Strategy: Machine learning uses pattern recognition to identify actionable opportunities to improve business outcomes.
  • Software Development: Developers are increasingly using machine learning to optimize product development, testing, and deployment.

Recently, machines have been performing tasks that were only possible through human intelligence with proper judgment and decision-making abilities. This feat is possible because of the pattern recognition abilities of machines that can analyze, learn, and use the knowledge in the future.

What Machine Learning Offers

At Crowdbotics, we understand Machine Learning to be the nexus where business, data, and experience meet emerging technology to work collaboratively. Machine learning offers an opportunity for traditional methodologies like statistics and data modeling to interface with algorithms and the computation capabilities of data science.

Teams, managers, and executives should ensure that they have a basic understanding of this ascendant technology. In the tech industry, it may even be advisable to offer a series of programs and modules designed to give students, executives, professionals, and employees an extensive understanding of how to use this technology to their benefit.

The three general types of machine learning include:

  • Supervised Machine Learning – This approach actively maps inputs to outputs to train the computer to optimize for desired outcomes.
  • Unsupervised Machine Learning - This approach allows the computer to process unlabeled data in an attempt to identify previously undetected patterns.
  • Reinforcement Learning - This approach defines desirable outcomes and then allows the computer to optimize its information processing in order to identify the most efficient route to these outcomes.

Common types of algorithms for machine learning include:

  • Representation: Analyzes a set of data and creates a structured model for understanding it.
  • Evaluation: Tests data for anomalies or necessary changes.
  • Optimization: Alters data to obtain better results from the program or device.

AI/ML Myths And Realities

Over the last few years, the hype around artificial intelligence has been exploited by several industries. Some businesses adopt AI in the belief that it can serve as a relevant innovation, when, in fact, it has very little value to their organization.

Many so-called AI products do not exceed the scope of simple predictions or classification capabilities using algorithms and fundamental statistical analysis. Due to this, the algorithm becomes less precise and less useful whenever a business has a sudden change or a rise in the data input stream.

Using machine learning algorithms as a primary tool for making strategic business decisions is a risky bet given the inflexibility of most modern AI applications. This can lead to a phenomenon occasionally referred to as "artificial stupidity", which is defined as a reduction in efficiency as a result of misapplied AI.

Conclusion

There are a large number of machine learning and artificial intelligence companies globally. For this reason, it is important to work with a partner who can not only build ML-infused products, but also help you understand the relevant applications for AI considering your business's unique requirements.

Crowdbotics is a trusted partner when it comes to AI and ML development. Our team of experts is well-versed in common ML applications, and the Crowdbotics platform utilizes AI to comb the universe of open source applications and select the best components for a given software build.

Are you looking to build an application powered by machine learning? Get in touch with our experts today.