Automation and AI are central to how we use computers today, and while these tools have made it possible for everyone from businesses to students to speed technical procedures, AI is becoming passé. According to technology experts, 2019 will see substantial growth in the area of deep learning accelerators, low-power alternatives to traditional AI that are optimized for specific tasks. Unlike non-specific AI solutions, these programs promise a faster, more comprehensive approach to machine learning that’s capable of everything from driving a car to detecting and eliminating malware.
One of the primary advantages of deep learning accelerators for machine learning is how they’re customized to a particular end. While most machine learning systems on the market today perform generic tasks and relies on a cumbersome set of directions, the underlying structure of accelerators is simple; Snowflake, for example, is a scalable system that uses a series of vector matrices to process the greatest amount of information per cycle, with overlapped data transfer and MAC compute time. The architecture is much more easily modified than ordinary AI which would require detailed code changes and overall rearrangement for every small modification.
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Cloud computing and deep learning accelerators are a perfect match, with both playing central roles in today’s operations. For example, machine learning is already used for preliminary category prediction, basic binary prediction, and supervised and unsupervised learning processes. Currently, though, in order to perform these tasks, companies must rely on inefficient, relatively slow, and often inaccurate systems. Machine learning can perform a task, but often with irregularities one wouldn’t find if a professional performed the task.
Now compare these basic processing tasks with the capabilities found in deep learning accelerators. While basic machine learning programs can sort items into a narrow set of categories, deep learning accelerators can use computer vision to classify an enormous variety of objects using neural networks. Similar computer vision capabilities are also crucial to self-driving car technology.
One practical application of deep learning accelerators is in the fight against malware. Like other cybersecurity threats, malware is constantly evolving, and it’s difficult for coders to respond proactively to new threats. Deep learning accelerators, however, can use a combination of techniques, including statistical, dynamic, and hybrid analysis to identify malicious software.
Deep learning accelerators do still have a number of problems in actual application, such as lack of transparency in decision making and lack of complete autonomy in modifying their analytic process. Despite these limitations, however, the programs still perform significantly better than traditional machine learning systems.
Despite the imperfections, deep learning accelerators are already part of our everyday lives. Google has been training a variety of programs in this mode, including advanced visual searches of unlabeled images, flight path prediction, and even programs used in astrophysics, medicine, and law enforcement. Because Google has access to such an enormous amount of data in countless forms, the company has been able to train deep learning accelerators on widely variable data functions, and we are already reaping the benefits. Though most of us will never design such a program, we will use them every day – both actively and as the underlying security infrastructure and file management system for our computers, bringing optimal function with every click.
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