Deep learning is having a moment. There was a time where we could only dream of partially autonomous vehicles and voice-activated assistants. Today, however, these inventions are a regular part of our lives. A subfield of machine learning (ML) and artificial intelligence (AI), deep learning algorithms are designed to learn like a human brain. Deep learning continually analyzes data using an advanced technology known as “artificial neural networks,” which are operated by a series of algorithms that can perceive complex relationships in data sets. These neural networks allow computers to see, hear, and speak—it is the reason we can talk to our phones and dictate emails to our computers.
Algorithms have always been part of the digital world, where they are trained and developed in perfectly simulated environments. The current wave of deep learning facilitates AI’s leap from the digital to the physical world. While the applications are endless—from manufacturing to agriculture—there are still challenges of accuracy, clean data, and reinforcement learning.
Deep Learning in the Real World
AI researchers are working to introduce deep learning to our physical, three-dimensional world. Experts anticipate that deep learning will advance several sectors over the next few years, including:
- Self-Driving vehicle capabilities: The handling of novel situations is the main problem for autonomous vehicle engineers. With growing exposure to millions of scenarios, a deep learning algorithm’s regular cycle of testing and implementation ensures safe driving. Global industry growth for autonomous cars is 16% a year. The global autonomous vehicle market reached nearly US$106 billion in 2021, and one forecast projects it will grow to US$2.3 trillion by 2030.
- Fraud news detection and news aggregation: Deep learning is heavily utilized in news aggregation, which attempts to tailor news to consumers’ preferences. Reader personas are defined with greater complexity to filter out content based on a reader’s interests, as well as geographical, social, and economic factors. Furthermore, there is always room for improvement in filtering out fake news and misinformation.
- Natural Language Processing (NLP): One of the most challenging things for computers to learn is how to comprehend the complexity of human language, including its syntax, semantics, tonal subtleties, expressions, and even sarcasm. The global market for Natural Language Processing (NLP) is expected to reach US$25.7 billion by 2027.
- Healthcare: Some of the deep learning projects gaining traction in the healthcare industry include assisting with the quick diagnosis of life-threatening diseases, addressing the shortage of qualified doctors and healthcare providers, and standardizing pathology results and treatment plans. By 2026, artificial intelligence has the potential to save the clinical healthcare business more than US$150 billion.
Getting “Data-Centric AI” with Deep Learning
Andrew Ng is among the pioneers of deep learning and, according to Fortune, he’s also one of the most thoughtful AI experts on how real businesses are using the technology. Ng has become a champion for what he calls “data-centric AI.” Ng believes developers and businesses should be asking questions like: What data is used to train the algorithm? How is it gathered and processed? How is it governed?
Data-centric AI is the practice of “smartsizing” data so that a successful system can be built using the least amount of data possible. If data is carefully prepared, a company may need far less of it than they think—saving both time and money .Calling it as important as the shift to deep learning that occurred over the past decade, Ng believes that the shift to data-centric AI is the most important shift businesses need to make today.
Be Prepared for Future of Deep Learning
As deep learning facilitates AI’s leap from the digital to the physical world, it is important to stay current with the latest technology advances. The IEEE Academy on Artificial Intelligence is designed for those who work in industry and need to understand new technical information quickly so they can apply it to their work. Learn more about the program>>
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Resources:
Placek, Martin. (16 January 2023). Size of the global autonomous vehicle market in 2021 and 2022, with a forecast through 2030. Statista.
Carsurance. (20 February 2022). 24 Self-Driving Car Statistics & Facts. Carsurance.
Global Industry Analysts, Inc. (April 2021). Natural Language Processing (NLP) – Global Market Trajectory & Analytics. Research and Markets
Gordon, Nicholas. (30 July 2021). Don’t buy the ‘big data’ hype, says cofounder of Google Brain. Fortune.
Ingle, Prathamesh. (9 July 2022). Top Deep Learning Applications in 2022. Marktechpost.
Fine, Ken. (15 January 2022). How digital experiences are fueling the new digital economy. VentureBeat.
Todorov, Georgi. (20 April 2022). 92 Stunning Artificial Intelligence Stats, Facts and Figures in 2022. Thrive My Way.
Woertman, Bert-Jan. (30 April 2022). Deep learning is bridging the gap between the digital and the real world. VentureBeat.
World Economic Forum. (20 July 2022). Is AI the only antidote to disinformation? The European Sting.
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.