Article
Machine Learning vs. Deep Learning: What’s the Difference?
In recent years, artificial intelligence (AI) applications have surged in popularity. Examples include text editors, facial recognition systems, and digital assistants. Simply put, AI enables machines to perform tasks that require intelligence. As a branch of computer science, AI contains several subsets, with machine learning and deep learning among the most common.
What is Machine Learning?
Machine learning algorithms parse data into smaller pieces, then recombine that data to learn and solve problems. Through this process, they make informed decisions. Engineers train machine learning systems with structured data, where patterns are clearly defined. Although models improve with limited supervision, they still need human guidance when they get stuck.
In practice, machine learning powers image and speech recognition, email spam filters, and predictions in weather and stock markets.
What is Deep Learning?
Deep learning builds on machine learning. Unlike traditional models, deep learning uses artificial neural networks to process unstructured data such as images. These networks run advanced algorithms that detect complex relationships in data, imitating the human brain.
For example, online retailers and streaming services use deep learning to recommend products and shows. It also drives facial recognition software and autonomous vehicle systems.
Deep learning represents a breakthrough. Unlike traditional approaches, it can solve problems in a single step and improve without human intervention. Many experts even call it the “backbone of true AI“.
GPT-3, CLIP, and DALL-E
Deep learning is propelling radical advancements in search engine technology and natural language processing (NLP) models—artificial intelligence that can automatically manipulate speech and text.
GPT-3
Last year, OpenAI, an AI research and deployment company, unveiled a breakthrough NLP model that applies deep learning to mimic human language. Known as Generative Pre-trained Transformer 3, or GPT-3, the system uses autocomplete technology, a feature in Google Search that can speed up searches by predicting what you are going to type next. (It does this by matching what you are typing to commonly searched for words and phrases).
Combining autocomplete technology with massive amounts of data gleaned from the internet, GPT-3 can generate text on its own. As an example, here is an article written by a GPT-3 application without human assistance.
CLIP
Similarly, OpenAI recently built a pair of new deep learning models dubbed “DALL-E” and “CLIP,” which merge image detection with language. As such, they can help language models such as GPT-3 better understand what they are trying to communicate.
CLIP (Contrastive Language-Image Re-Training) is trained to predict which image caption out of 32,768 random images is the right caption for a specific image. It learns image content based on descriptions instead of one-word labels (like “dog” or “house”.) It then learns to connect a wide array of objects with their names in addition to words that describe them. This allows CLIP to identify objects within images outside the training set, meaning it’s less likely to be confused by subtle similarities between objects.
DALL-E
Unlike CLIP, DALL-E doesn’t recognize images—it illustrates them. For example, if you give DALL-E a natural-language caption, it will draw a variety of images that matches it. In one example, DALL-E was asked to create armchairs that looked like avocados, and it successfully produced a number of different results, all which were accurate. See a picture of the results here.
“The thing that surprised me the most is that the model can take two unrelated concepts and put them together in a way that results in something kind of functional,” Aditya Ramesh, one of DALL·E’s designers, told MIT Technology Review.
Making Deep Learning Cheaper, Faster, and More Efficient
While deep learning can deliver impressive results, it has some limitations. For example, it has vast data and mathematical processing needs that consume enormous amounts of energy. Deep learning also relies on more sophisticated hardware than traditional machine learning systems, in addition to requiring extensive data and lengthy training times.
However, engineers are working on innovative solutions to this problem. For example, Lightmatter, a company that makes next-generation computing platforms for artificial intelligence systems, has developed a neural-network accelerator chip that can make calculations more efficiently by using photons instead of electrons. While the technology isn’t currently as precise as today’s chips, it represents a step forward in the quest to make deep learning cheaper, faster, and more efficient.
As machine learning and deep learning evolve, they drive advancements in autonomous vehicles and the Internet of Things. Looking ahead, these technologies will continue reshaping industries.
Understand Machine Learning
Machine learning is a vital aspect of artificial intelligence (AI). Because machine learning allows AI systems to learn from experiences without needing explicit programming, it’s key for the future of AI technology.
Check out these new courses on machine learning, available on the IEEE Learning Network today.
Machine Learning in the Age of Enterprise Big Data
Machine Learning in a Data-Driven Business Environment
Machine Learning Algorithms, Models, and Systems Integration
Resources
Schneider, David. (8 January 2021). Deep Learning at the Speed of Light. IEEE Spectrum.
Douglas Heaven, Will. (5 January 2021). This avocado armchair could be the future of AI.
MIT Technology Review.
(17 December 2020). The Difference Between Deep Learning and Machine Learning. Tech Funnel.
(20 May 2019). Deep learning & Machine learning: what’s the difference? Parsers.
Grossfeld, Brett. (23 January 2020). Deep learning vs machine learning: a simple way to understand the difference. ZenDesk.
Thursday, 14th January 2021