Artificial intelligence, Machine Learning, or Deep Learning?
With the various terms surrounding Artificial Intelligence (AI) and use cases in business today, it is hard to keep up with all of the new innovation across industries. As AI technology and techniques continue to evolve around us, so do the businesses that use them.
Artificial Intelligence can be simply defined in one sentence as the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence. TowardsAI reports, in contrast to Machine Learning, AI is a moving target, and its definition changes as its related technological advancements turn out to be further developed.
Machine Learning is one of the ways we expect to achieve AI. Deep Learning is a very specific category within Machine Learning, yet Machine Learning is a specific set of techniques within a broader range of Artificial Intelligence. While Deep Learning seems to be all of the hype right now, the field of artificial intelligence is much broader than deep learning and even machine learning.
The correct approach for a particular problem does not always require the most sophisticated methods. Typically Deep Learning needs a plethora of data, while more simple Machine Learning techniques need much less. When developing self driving cars, Deep Learning techniques are certainly a necessity. However, a problem statement such as property price prediction, might be best utilized with more simple Machine Learning techniques or even linear regression.
Software Platforms for AI Development
With the many AI platforms today, it is difficult to follow the new developments across various fields. When challenges arise, stick to the fundamentals. AWS and other cloud providers offer helpful well-known starting points for companies, such as Amazon Sagemaker. In terms of programming languages, Python remains the most popular and recommended language today.
Deep Learning and Real-World Use
Think of Deep Learning as a specialized set of techniques within the broader Machine Learning space. Deep Learning tries to mimic the learning behavior of how humans learn a new topic over time. For instance, to teach someone how to recognize a chair you might first teach the basic idea of geometry (lines and circles), followed with complex shapes that represent real world objects. Deep Learning attempts to take a similar approach to mimicking sophisticated levels of learning through multilevel neural networks.
Typically Deep Learning is most applicable for very complex Machine Learning tasks, but also for when you have the option to learn from lots of data. This isn’t practical for many applications, but works well when there is a large amount of data available. Some of the most popular applications of Deep Learning include self driving cars, search engines such as Google, facial recognition by Apple, and similar applications.
One of the biggest misconceptions about AI surrounds the current availability of open source tools and how developing AI is ‘very easy’ now. As well as, how anyone with a basic software engineering background can build AI applications from scratch.
While barriers to entry have been significantly lowered and building “hobby” projects is now far easier, building and maintaining an AI application in business still requires a fair amount of experience and background in AI. This is often far more expensive and time consuming than the majority of organizations believe.
Missing or Corrupted Data in AI
Data cleaning, labeling, and handling missing data is unfortunately the least fun. However, it is also where the most time in AI development is spent. There is no one-size-fits all answer, and these real world issues are also where experience matters.
Techniques can be used to understand if the data mimics the real world closely enough or if gaps exist in specific areas. Once gaps are identified, steps can be followed to fill in the missing data or figure out how to handle the corrupt data.
Missing or corrupt data is also a good area to reach out for external experts. This ensures internal teams can hyper focus on building the business logic of the application and not spend their time on data issues, which do not directly impact business.
Data Visualization Libraries
Finding the right data visualization library is very crucial in the development process of AI. Data visualization refers to how we represent our data with visuals such as charts, graphs, and more. Two recommended libraries include deck.gl and kepler.gl for visualizations. Both of these tools can be used internally and are widely known as the top tools utilized by companies today.
Staying Relevant in the AI Revolution
When building AI capabilities it is critical to pay attention to the underrated components of AI, which include data and data engineering. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis.
To stay relevant in the ever changing world of AI and before blindly jumping on the bandwagon ask yourself, are you collecting critical data about your business operations? Is the data effectively stored and organized? Lastly, Is the data easily accessible?
While AI is a current tech buzzword, at the end of the day it is only useful if it solves a business problem. Collaborating with the right individuals will help unlock the potential of AI within an organization.
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