Facebook uses it, Netflix uses it, Google uses it, and the list goes on and on. Today, machine learning and deep learning are versatile and powerful and two exciting technologies of the 21st century.
A couple of years back, machine learning (ML) and deep learning (DL) were science fiction, but today their application in real-world industries is limited to human imagination.
DL became an overnight star when a robot beat a human player in the popular yet challenging game of AlphaGo. Powered by data science, these technologies have made life much easier.
When properly trained, these technologies can finish tasks more efficiently than humans.
Staying up-to-date with the recent machine and deep learning technology innovations is essential for businesses to plan their course of action for conducting their business.
Before understanding the future of DL and ML, let’s explore how these terms differ and how they might affect the functioning of a business.
What is the difference between deep learning and machine learning?
Understanding the difference between deep learning vs machine learning is essential to know which technology might cause rippling effects in your business and how you can use it.
Machine learning works on the principles of computer algorithms. It relies on various algorithms and trains computer systems on historical data to ensure they can make accurate predictions and decisions on their own.
DL helps process unstructured data such as images, documents, and text. In layman’s terms, DL is the subset of ML, which is a subset of artificial intelligence (AI). On the other hand, deep learning links ML algorithms such that the output layer of one algorithm is received as the input layer of another.
To understand and analyze the analogies between these two terms, let’s explore an example:
AI can be the running shoes that utilize technologies to boost athletes’ running performance. Lightweight and comfortable shoes make it easier for athletes to run faster. ML could be training shoes with specialized and comfortable foam cushioning. DL could be shod with carbon-fiber plates inside the comfortable foam that help athletes run faster. Such shoes use state-of-the-art technology inside an AI framework, which helps athletes run fast.
Future of machine learning
The future of machine learning is exceptionally bright because almost every industry uses ML to speed up its growth. From education and digital marketing to healthcare and search engines, ML is helping every industry automate and reduce the percentage of manual tasks performed by employees.
Industries to watch for machine learning disruption
Here are some industries that are likely to witness disruption because of ML technology:
Pharmaceutical and healthcare industry
ML has endless applications in the healthcare and pharmaceutical industry. With new viruses and diseases popping up now and then, ML can help identify and diagnose diseases before they become a pandemic. Companies can use this intuitive technology to contribute to better treatments and predictions.
Many cancer and genetic diseases are difficult to detect or recognize, but ML can effectively handle them and save a patient’s life. What’s more interesting is that ML can predict the impact of drug and compound structure on different genes and cells.
ML can even speed up a company’s drug testing time.
Manufacturing sector
Though the manufacturing sector is the first to adopt changing technology, this sector is a slow adaptor to ML technology. Manufacturing companies are using predictive algorithms for planning machine maintenance.
ML enables predictive monitoring using ML algorithms that help in forecasting equipment breakdowns. It helps in scheduling timely maintenance. The technology reduces cost, improves supply chain management, and enhances quality control.
Automotive industry
Tesla and Honda are a few companies in the automotive industry exploring the possibility of deploying self-driving cars.
ML is a technology that can convert the dream of self-driving cars into a reality.
Data science and ML can help car makers remain competitive by improving everything from research to design manufacturing to marketing processes. In the automotive industry, ML is much more than self-driving cars.
Future trends in machine learning
Some trends and innovations in ML that bring a revolutionary change are:
No-code machine learning
No-code machine learning is an innovative way of programming ML applications without going through the long process of designing algorithms, modeling, and pre-processing. The future of ML is bright because no-code ML is easier to use and eliminates the requirement for longer development time.
Without the need to write any code, a company spends less time on debugging and most of their time getting results.
TinyML
Large-scale ML applications have certain limits, paving the way for smaller-scale applications. In the coming years, companies will probably use smaller ML programs to achieve lower power consumption, latency and bandwidth and ensure user privacy.
ML programs driven by IoT solutions ensure privacy since the device computes locally. This innovation finds excellent application in healthcare, agriculture, and industrial centers.
These industries can use TinyML algorithms with IoT devices to track and make predictions about collected data.
AutoML
Similar to no-code ML, AutoML focuses on making ML more accessible to developers. It can bridge the gap by providing simple and accessible solutions that do not rely on ML experts.
Another benefit that AutoML is likely to bring is improved data labeling tools and the possibility of automatic tuning of neural network architecture.
This technology can automate your entire labeling process, reducing the risk of committing errors. It reduces labor costs, allowing companies to focus on data analysis.
Future of deep learning
DL is a disruptive technology because it can do things humans can do. For instance, identifying bridges in a picture or driving a bike. Companies are likely to use DL to automate predictive analytics. Often, a company can use DL to identify and determine customers’ buying behavior and patterns. This can increase the company’s sales and help you keep more customers.
On Amazon’s website, do you see items such as ‘frequently bought together’ when purchasing a new toy for your kid? These are primarily based on predictive deep learning algorithms.
These algorithms consider your past and current search pattern to suggest additional products a customer might require.
DL tools will probably incorporate a simplified programming framework for fast coding in the coming years. In the future, deep learning developers will adopt integrated, cloud-based development and an open environment that provides access to algorithm libraries.
It can even enable API-driven development, such as composable containerized microservices.
Future trends in deep learning
Some future trends in deep learning are:
News aggregation and fraud news detection
While there is no way to filter out harmful and ugly news from your social media newsfeed, extensive use of DL can help companies customize news as per readers. Also, with the internet becoming a primary source of all genuine and fake information, DL can end all this to some extent.
Deep learning helps develop classifiers that detect incorrect or fake news and remove it from social media feeds. It can even warn companies of possible privacy breaches and prevent fraud detection.
Virtual assistants
A noteworthy application of deep learning is virtual assistants. Popular virtual assistants like Alexa, Siri, and Google Assistant make use of DL. The modern-day virtual assistants use deep learning to know more about their subjects.
These assistants can translate speech to text, book appointments, and make notes for you. DL helps understand your commands by evaluating natural human language to execute them.
Detecting development delay in children
Another area where the future of deep learning seems bright is detecting development delays in children. This technology can help in the early detection and course correction of problems associated with children and infants.
Pharmaceutical companies can identify language, and speech disorders with DL even before the child reaches kindergarten. DL will bring a revolutionary change in the pharmaceutical industry in the coming years.
Key takeaways
Today, businesses that adapt to machine learning and deep learning are the ones that do not fall prey to digital Darwinism. Such businesses are more likely to come up with breakthrough and innovative products that can bring a revolution in their industry.
Make sure your business is ready to make the most of what the future holds for machine learning and deep learning.