UK-based Cambridge Quantum Computing (CQC), a quantum software and algorithm specialist, today released researcher papers on its use of quantum computing to develop intuitive, “meaning-aware” natural language processing (QNLP).
A focal point of artificial intelligence inquiry, NLP that is contextual, that comprehends emotion, nuance, even humor, is NLP’s most advanced and challenging form.
In this video, Cortical.io CEO Francisco Webber demonstrates how the company’s software running on Xilinx FPGAs breaks new ground in the field of natural language understanding (NLU). “Cortical.io delivers AI-based Natural Language Understanding solutions which are quicker and easier to implement and more capable than current approaches. The company’s patented approach enables enterprises to more effectively search, extract, annotate and analyze key information from any kind of unstructured text.”
In this new whitepaper from our friends over at Exxact Corporation we take a look at the important topic of deep learning for Natural Language Processing (NLP) and choosing the right GPU for the job. Focus is given to the latest developments in neural networks and deep learning systems, in particular a neural network architecture called transformers. Researchers have shown that transformer networks are particularly well suited for parallelization on GPU-based systems.
MIT researchers have developed a new general-purpose technique sheds light on inner workings of neural nets trained to process language. “During training, a neural net continually readjusts thousands of internal parameters until it can reliably perform some task, such as identifying objects in digital images or translating text from one language to another. But on their own, the final values of those parameters say very little about how the neural net does what it does.”
[SPONSORED GUEST ARTICLE] These are the times that try the souls of IT staffs tasked with deploying AI at scale… Effectively implementing AI at scale involves a whole host of design, build, deployment, and management considerations. It requires ….
CIOs and tech leaders must consider ethical implications and make informed, responsible decisions In this Tech Insight Report, we break down the most pressing ethical issues surrounding artificial intelligence and machine learning and offer expert guidance to navigate this new, quickly evolving landscape.
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