- Transformers are a neural network that tracks context in sequential order, potentially revolutionizing language models and real-time translations.
- Deep learning models, like those used in Siri and Alexa, process data to recognize complex patterns and serve as the backbone for transformers.
- Transformers apply the concept of attention to neuron-like structures, allowing them to perform more efficiently than standard deep learning models.
Transformers look to be a revolution for machine learning, but how do they compare to deep learning? Artificial intelligence has been making waves online for the last year. The work stretches back further, however.
Models tend to use things like deep learning, where an AI is taught to think and adapt like a person. Transformers are a similar take on the same methodology, but could possibly be more human in response.
So, with all this in mind, where do they differ? They can both reason like a human mind, but transformers have gained more prominence recently. So, it’s time to dive in and see what is shaping the landscape of artificial intelligence in new and exciting ways.
What Is a Transformer?
Transformers are a neural network that tracks context in sequential order. Think of it as someone reading a sentence and then deriving its meaning from each subsequent word. The impact of a transformer on language models is looking to be a rather significant one.
AI models have driven machine translation for some time, but very few previous models could do it in real time. This isn’t just applicable to written text, either; you could see on-the-fly translations of the spoken word.
The potential for transformers is vast because they can interpret data as it is fed. This is in stark contrast to previous models, which take data in blocks and then have to connect the dots. Transformers break up the data into smaller blocks and handle interpreting the context as it processes them.
A transformer model can look at each possible data point and determine which one deserves more attention. This is built into the models themselves using an aptly named attention mechanism that serves as the core of its learning process.
This translates to faster means of processing data points. Current AI models can take millions to trillions of data points and hundreds to thousands of man-hours to get to a workable state. But think of how something like a transformer could change the way people interact with their smart assistants.
Your version of Siri on your iPhone is likely different from the one used by a friend or relative. It is tailored to your needs, mostly through constant use. Transformers could expedite the process.
What Is Deep Learning?
Deep learning has been a driving force behind much of the modern world. Smart assistants like Siri and Alexa both utilize deep learning models, for example.
At its core, it functions in a similar way to a person learning something. A deep learning model is fed data to process and recognize complex patterns. This extends to speech, images, and a host of other data sets.
Deep learning has been one of the building blocks of modern artificial intelligence and serves as the backbone for transformers. Current deep learning models weigh inputs identically and then derive context and patterns from the data sets.
From equally weighted data points, deep learning is able to construct its own logic. This has been a boon for platforms like autonomous driving, but there are still inherent flaws to the logic at play. Deep learning models still need human input to help identify objects; for example, in regard to visual annotation for self-driving cars.
Transformers vs Deep Learning
As previously mentioned, transformers and deep learning models are cut from the same cloth. Their approach greatly differs, however. While both methods take a closer look at the context behind their inputs, the way they derive the context greatly differs.
Deep learning models are closely modeled on the mathematical concepts inherent to the brain’s neurons. In function, this has been an efficient means of handling large data sets and developing a logical model behind it.
Transformer models take the concept of a neuron and apply the concept of attention to it. Consider how you read something. You aren’t weighing every individual word in a paragraph to derive its meaning. You’ve likely known how to read since your early childhood.
Instead, you are seeking the greater context which is applicable to a passage. Transformer models operate in a similar fashion to this and can perform far more efficiently than a standard deep learning model.
Which One Is Better?
Transformer models were first theorized in 2017, and have only recently begun to pick up steam on the wider AI development landscape. As such, it is still relatively early in their life cycle to determine if one is better than the other.
Theoretically, the transformer model is more effective, and could potentially be far less resource intensive than a deep learning model. The curious can see them in action with the AI concepts on offer from Hugging Face.
Hugging Face had previously used large language models, or LLMs, as the basis for their deep learning process. Transformers have been introduced to the pipeline, so you can see them in action for yourself if you have experience in the field.
Deep learning models have been the norm for a number of years. If you drive a Tesla, own an Amazon Echo, or use an iPhone daily, you can see the efficacy of deep learning.
AI firms aren’t likely to totally abandon their current deep learning models, especially when considering the sizable investment in resources and man-hours to develop them. Instead, they will likely be further integrated with transformers to expedite certain processes.
As to which is better, that is difficult to say. Transformers aren’t likely to completely replace other deep learning models. Instead, developers might implement them into their current workloads.
Transformers could very well speed up the processes by how low-code or no-code artificial intelligence models work. Legacy deep learning models might serve as the backbone, but a combination could very well be AI changing into something far more robust.
What Is the Future of AI?
Artificial intelligence, much like any tech field, is one of constant advancement. Compare the likes of GPT- 4 to the hobbyist Markov Chains of 2017 and it is quite a stark difference. Transformers are likely to make a massive impact on artificial intelligence.
It should make a marked difference in making visual distinctions between objects, a field where deep learning models currently struggle. Autonomous driving has been making great strides, but is hindered by a combination of latency and lack of clarity for its visual annotation models.
Transformers could expedite this process tremendously. The attention mechanism which acts as the core of a transformer model could be trained to discern which objects on the road to pay closer attention to, and which aren’t a pressing concern.
This is just one application where a transformer model introduced to a workload could have a massive impact on its current performance.
Artificial intelligence is a wide-spanning discipline with a variety of applications. What it means for modern life hasn’t been fully ascertained, but its current applications are enormously helpful for the sake of convenience.
It could be said that AI isn’t fully ready for more diverse workloads, like creative reasoning. The introduction of transformers to current deep learning models might very well change that, though.
The differences between the two methodologies are rather scant, save for the attention mechanism. The addition of human-like reasoning to AI models could very well lead to implementations that are shockingly human in the way they solve problems.
What this means will take time and hard work to fully absorb, however. At any rate, humanity is likely years out from seeing fully automated self-driving cars and other applications. Transformers could be the first step in a new AI revolution.
|Function||Tracks context in sequential order||Processes and recognizes complex patterns|
|Efficiency||Potentially more efficient||Less efficient|
|Applications||Language models, real-time translations, visual distinctions||Smart assistants, autonomous driving, image recognition|
|Future Integration||May be combined with deep learning models||May integrate transformers for improved performance|
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