Is your company ready for No-code AI?- Issue 52: Speaker Naturally, Chat Bots, NLP and Voice Briefing

Speaking Naturally

There’s so much machine learning and natural language processing happening  – but is it good for the environment? The journal Science recently reported that the total amount of energy consumed by data centers made up about 1% of global energy use over the past decade. Little wonder when you think that one recent natural language processing model was trained on a data set of 40 billion words!

So a group of researchers from the UK and France, set out to explore the environmental impact of a different form of training—called federated learning—and discovered that it had a significantly greener impact.

Should we be doing more to find energy-efficient approaches to training AI models?




P.S. Machine learning extraordinaire & Amazon CTO, Dr. Werner Vogels is speaking at CogX Festival 2021. Buy your pass now!

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Instead of training the models in data centers, federated learning involves training models across a large number of individual machines. The researchers found that this can lead to lower carbon emissions than traditional learning.

Read more (Tech Xplore)


Primer has developed a no-code platform it calls Automate. It allows a non-expert to take data from something like a Microsoft Excel spreadsheet and, in about 20 minutes, train an A.I. system to perform some key NLP tasks at accuracies that can approach human-level.

Read more (Fortune)

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Google is adding more information to your search so you can better understand where your site’s results come from. The company makes it easier for people to evaluate information and check sources for results displayed on large search platforms, the company said.

Read more (California News Times)


Conversations with chatbots are getting a lot more interesting. Fueled by artificial intelligence and natural language processing, chatbot technology has taken leaps forward over the past few years.

Read more (Electropages)

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Consumer-friendly voice assistants (VA) were introduced in 2011 by the launch of Siri followed by Alexa in 2014. These systems are being continually refined using machine learning, which is optimising the way that machines can understand human voices as well as giving AI a better ‘bedside manner’.

Read more (City A.M.)


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