Salesforce


Will Artificial Intelligence Replace Marketing Departments?

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For many in B2B and B2C marketing, once they get past the rush of new technology, some are wondering how long it will take to automate the entire marketing process (including their job). Michelle is Act-On's Chief Marketing Officer, and oversees the company's brand, demand, and customer expansion marketing efforts. Michelle comes to Act-On with 17 years' experience helping market leading companies, including Salesforce and Oracle, connect customers with technology solutions to grow their business. Prior to her tenure at Salesforce, Michelle was a Senior Director at Oracle and a Senior Product Marketing Manager at Stellent (acquired by Oracle).


How to use artificial intelligence to improve performance

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There is a misconception that artificial intelligence will replace humans. Great marketers, for instance, spend a great amount of time creating content strategies. Instead of spending so much time deliberating, marketers could be using artificial intelligence to do the job. A machine can look at every individual's behaviour and use reliable and proven data to inform the marketers of the right content and channel for specific individuals, at the right time.


Salesforce Einstein – CRM Stepping Into The World Of AI

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But the only solution to the query how the unfathomable data can be converted to a meaningful stream of data generating valuable insights is Artificial Intelligence. So if Salesforce is put into appropriate use then it can act like an extremely suited tool to feed the data to Einstein from the customer and social data to the activity data (i.e from email, Chatter, calendar, etc). Einstein has its advanced machine learning concepts along with deep learning, natural language processing, and predictive analysis and all these makes it possible for it to create a customized model without any manual involvement for each and every constituent. The teams will be more eligible to deliver more timely next generation proactive service and suggestions.


Understanding the Artificial Intelligence Hype Cycle, in 5 Stats

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We're witnessing an arms race that involves just about every major technology company, from martech giant Salesforce to the'Chinese Google' Baidu to old dogs like Microsoft. In Gartner's hype cycle, which ranks technologies based on how the market perceives them and how far away they are from mainstream adoption, machine learning is right at the tippy top. The research firm expects companies driven by insights (i.e., from data and AI) to significantly outperform those that don't or can't. Yet marketers should be happy that every martech giant is investing heavily in machine learning.


Understanding the Artificial Intelligence Hype Cycle, in 5 Stats

#artificialintelligence

We're witnessing an arms race that involves just about every major technology company, from martech giant Salesforce to the'Chinese Google' Baidu to old dogs like Microsoft. In Gartner's hype cycle, which ranks technologies based on how the market perceives them and how far away they are from mainstream adoption, machine learning is right at the tippy top. The research firm expects companies driven by insights (i.e., from data and AI) to significantly outperform those that don't or can't. Yet marketers should be happy that every martech giant is investing heavily in machine learning.


3 types of artificial intelligence, but only 2 are valid

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Current AI applications can be broken down into three loose categories: Transformative AI, DIY (Do It Yourself) AI, and Faux AI. Transformative AI turns data into insights and insights into instructions. While there is indeed a finite set of actions involved in driving, the data set the AI must process shifts every single time the passenger gets into the car based on road conditions, destination, route, oncoming and surrounding traffic, street lanes, street closures, proximity to neighboring vehicles, a pedestrian stepping out in front of the car, and so on. DIY AI is any artificial intelligence platform whose end goal is to make you, the user, more informed so that you can then do the remaining work yourself.


Machine Learning & AI: When to Start?

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When to start using machine learning in your business is not a hypothetical question; it's a question you must answer today. So here are a few thought starters to help you explore your machine learning investment strategy. Set up an in-house system to ensure that you are aware of all of the commoditized machine learning productivity tools that are likely to impact your business. Today is a good day to start thinking about investing in machine learning tools for your business.


3 types of artificial intelligence, but only 2 are valid

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Transformative AI turns data into insights and insights into instructions. DIY AI is any artificial intelligence platform whose end goal is to make you, the user, more informed, so that you can then do the remaining work yourself. In other words, the AI is reading companies' CRM data, making sense of it, and setting up sales people for more success than they'd have if they had to wade through the same data on their own. Programmatic ad buying is a good example of an insights-driven, predictive technology that many people confuse with AI -- and which often passes itself off as the same.


How Artificial Intelligence will impact professional writing

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An AI algorithm developed by researchers at Salesforce generates snippets of text that describe the essence of long text. These tools can help writers skim through a lot of articles and find relevant topics to write about. "Since new semantic technologies are now mature enough to read human language, journalists and professional writers can finally go back to writing for people," Cuofano says. "The next revolution (which is already coming) is the leap from NLP to a subset of it called NLU (Natural Language Understanding)," Cuofano says.


Salesforce Announces AI Breakthrough, Reducing Information Overload

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Salesforce today announced the results of a research project tackling one of the most difficult natural language processing (NLP) research challenges--generating long, coherent, and meaningful text summaries. The new deep learning approach Salesforce researchers Romain Paulus, Caiming Xiong and Richard Socher experimented with, promises to help address the challenge of information overload we all suffer from today. In the animated example above, you can watch how the Salesforce text summarization model generates a multi-sentence summary from a news article. For each generated word, the model pays attention to specific words in the article being summarized and the previously generated words and sentences.