Artificial intelligence (AI) and machine learning are two of the most important technology trends to emerge in recent years. To call them trends is a disservice; AI and machine learning are poised to change the way we live in the 21st century.
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AI and machine learning in everyday life
In fact, AI and machine learning have already changed our lives, even if we’re not aware of it. Below are some examples of how we currently use AI and machine learning in our daily lives:
- Facial recognition for smartphones and apps
- Social media
- Emailing and messaging
- Search engines
- Digital voice assistants, such as Siri and Alexa
- Smart home devices
- Travel apps
- Banking
- Shopping
- Streaming services
AI and machine learning systems are developing rapidly. As they grow increasingly important in our lives, so too does the role of software engineering in developing them.
Software engineering: The power behind the throne?
Software engineers are vital in designing and implementing algorithms that allow machines to learn and make decisions. When developing AI and machine learning systems, software engineers create algorithms that ensure maximum efficiency, scalability and reliability when in operation. Software engineers also test algorithms to make sure they work properly and are optimized for performance.
In addition to the crucial work of designing and testing algorithms to work in AI and machine learning systems, software engineers work to create standardized frameworks and tools to make systems faster and easier to use for people working in other areas of AI research and development.
The high demand for AI, machine learning and software engineers
Software engineers and data scientists are in a unique position when it comes to emerging technologies such as AI and machine learning systems. There has been a massive adoption of these systems by society, and that is expected to increase even more in the future. AI growth is expected to reach $500bn in 2023. There is an increasing demand for software engineers across all industries. Given their specialist knowledge and the growing demand for them, software engineers can often command high salaries in the job market. It’s no wonder, then, that more and more people are considering computer science degrees to prepare themselves for a career as a software engineer.
For those considering pursuing a career in computer science, an online Master’s in Computer Science obtained through a reputable institution such as Baylor University, provides excellent preparation. Ideal for those who wish to continue working as they learn, this 100% online course offers both data science and software engineering tracks. Considering its reputation and standing, it’s no surprise Baylor University has its finger on the pulse of another emerging technology: deep learning.
Discussions on machine learning and deep learning in Baylor’s Master’s in Computer Science program show the school’s future thinking, but right now, you may be wondering, “What is deep learning?”
Deep learning vs. machine learning
To understand deep learning, we must distinguish it from machine learning. While deep learning is a branch of machine learning, its approach to data is different. Where machine learning normally relies on a programmer training it to analyze structured or labeled data, the algorithm in deep learning chooses the relevant features based on the raw data it’s given. A deep learning network often improves with the amount of data used to train it.
The “deep” in deep learning refers to the multiple node layers used in a single neural network. Baylor’s article on deep learning compares each node to how parts of the human brain control different functions. A node extracts specific features for classification. With an input layer at the start and an output layer at the end, three or more node layers combine to form deep neural networks.
One benefit of deep learning networks is that they are adept at processing larger, more complicated sets of raw data. Where machine learning relies on structured data, deep learning works better with unstructured data. Deep learning doesn’t require lengthy feature engineering, and it doesn’t require data labeling. Deep learning is adept at solving complex problems in a shorter space of time than machine learning, which requires more of the human touch applied by a data scientist or software engineer to help find solutions.
How are deep learning systems used in daily life?
Deep learning, like AI and machine learning, can be used in a variety of everyday situations. As other technologies evolve, deep learning systems will evolve to help provide solutions to the problems that arise along the way. For now, though, here are some of the areas where deep learning is currently being applied:
Autonomous vehicles
Deep learning networks synthesize real-time data from onboard sensors and cameras to help autonomous vehicles operate safely on the roads.
Fraud detection
Deep learning tools, such as auto-encoders and generative adversarial networks (GANs), add value where manual fraud checks and basic software are not enough to detect fraudulent transactions.
Medical diagnosis and research
In the field of medical diagnosis and research, deep learning networks can alert primary care providers and specialists to anomalies such as brain hemorrhages and cancers, as well as streamline preclinical and clinical research.
Looking forward to the future
While the above descriptions only scratch the surface of deep learning, hopefully they have given you a better idea of what deep learning is and its potential uses in our everyday lives. The application of AI, machine learning and deep learning is becoming increasingly common. There are now countless companies — Amazon, Apple, Facebook and Google are the most obvious examples — that offer services to consumers based on AI, machine learning and deep learning systems. It appears these systems are here to stay.
It’s tempting to believe AI and machine learning will run away with their own development. It’s worth remembering, though, the important role of software engineers in designing, testing and optimizing algorithms to be used in AI and machine learning applications. The exciting nature of the work and the high demand from companies mean there is no better time to consider a career in software engineering.
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