Is it Machine Learning? Or is it AI? Much of what we term AI today results from the application of Machine Learning to extraordinarily large amounts of data.
To be precise, it is the application of so-called Deep Learning techniques that has enabled the rise of voice search and voice-activated assistants such as Siri, healthcare innovations in areas such as cancer diagnosis and treatment, face recognition such as AWS Rekognition and the broader areas of image and video analysis and recognition, machine translation including tools like Bing Translator, speech recognition tools and the emergence of the so-called self-driving automobiles and more.
Technically, we should call this the Deep Learning resurgence, and not the AI resurgence.
In simple terms, Deep Learning is a Machine Learning technique that teaches computers to do what comes naturally to humans i.e. learn from examples.
With Deep Learning, a computer model learns to perform classification tasks directly from images, text, or sound et al.
The Deep Learning models are trained by using a large set of labeled data and neural network architectures that contain multiple layers of software modeling the behavior of human neurons.
Machine Learning's Achilles Heel TodayIn order to train a Deep Learning model today, there are two techniques that while effective today will eventually become the bane of AI.First, conventional learning approaches use an approach requiring the training data to be centrally aggregated on a single system.
Together, these two critical capabilities have the potential to enable today's Machine Learning implementations to address their Achilles Heel and to enable AI applications that are both not privacy intrusive and not susceptible to the single-vendor Byzantine Faults.
What's next Blockchains + Machine Learning open up a disruptive new approach to take AI mainstream, whilst safeguarding user privacy and ensuring vendor-neutral applications that mitigate risk.
It is critical that the industry at large address put user privacy above any commercial interests and use blockchain capabilities to build the machine learning models that enable their AI applications.
How blockchain technology can save AI
pubblicato su Oct 28, 2019
by Cryptoslate | pubblicato su Coinage
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