TGI Fridays doubled business and grew engagement 500% with AI (VB Live) | VentureBeat

In the past 14 months, overall engagement for restaurant chain TGI Fridays has grown more than 500 percent, says Sherif Mityas, the company’s chief experience officer. And with personalized outreach, they’ve doubled online business in the last year, to the tune of millions of dollars.

“AI has created a tremendous impact in driving our off-premise business in the last year,” Mityas says. “Everything we do that collects, captures, analyzes, and utilizes data for the benefit of our guests is a critical component of our marketing strategy, while using artificial intelligence to help us personalize engagement is critical to our success.”

Ethics and the pursuit of artificial intelligence

Capture.JPGIn the AI arena the stakes are extremely high and it is quickly becoming a free-for-all from data acquisition to the stealing of corporate and state secrets. The “rules of the road” are either being addressed along the way or not at all, since the legal regime governing who can do what to whom, and how, is either wholly inadequate or simply does not exist. As is the case in the cyber world, the law is well behind the curve.

Ethical questions abound with AI systems, raising questions about how machines recognise and process values and ethical paradigms. AI is certainly not unique among emerging technologies in creating ethical quandaries, but ethical questions in AI research and development present unique challenges in that they ask us to consider whether, when, and how machines should make decisions about human lives – and whose values should guide those decisions.


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‘The Beginning of a Wave’: A.I. Tiptoes Into the Workplace


The bots are mainly observing, following simple rules and making yes-or-no decisions, not making higher-level choices that require judgment and experience. “This is the least intelligent form of A.I.,” said Thomas Davenport, a professor of information technology and management at Babson College.

But all the signs point to much more to come. Big tech companies like IBM, Oracle and Microsoft are starting to enter the business, often in partnership with robotic automation start-ups. Two of the leading start-ups, UiPath and Automation Anywhere, are already valued at more than $1 billion. The market for the robotlike software will nearly triple by 2021, by one forecast.

“This is the beginning of a wave of A.I. technologies that will proliferate across the economy in the next decade,” said Rich Wong, a general partner at Accel, a Silicon Valley venture capital firm, and an investor in UiPath.

The emerging field has a klutzy name, “robotic process automation.” The programs — often called bots — fit into the broad definition of artificial intelligence because they use ingredients of A.I. technology, like computer vision, to do simple chores.

For many businesses, that is plenty. Nearly 60 percent of the companies with more than $1 billion in revenue have at least pilot programs underway using robotic automation, according to research from McKinsey & Company, the consulting firm.


Full article: proud to have supported the first summer camp of Women in AI


What a great time meeting young amazing girls (14-18 years old) and sharing with them my story.

Great questions included:

  • Why did you started?
  • What made you believe that you could?
  • What are you the proudest about?
  • Any great tips for a young girl?


About woman in AI :

We are a group of passionate international women who are experts and self-trained in the field of Artificial Intelligence. We felt the lack of diversity of women in this field and we want to change it.

Artificial Intelligence and Bad Data


Facebook, Google, and twitter lawyers gave testimony to congress on how they missed the Russian influence campaign. Even though the ads were bought in Russian currency on platforms chalk full of analytics engines, the problematic nature of the influence campaign went undetected. “Rubles + US politics” did not trigger an alert, because the nature of off-the-shelf deep learning is that it only looks for what it knows to look for, and on a deeper level, it is learning from really messy (unstructured) or corrupted and biased data. Understanding the unstructured nature of public data (mixed with private data) is improving by leaps and bounds every day. That’s one of the main things I work on. Let’s focus instead on the data quality problem.

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