Nvidia explains how true adoption of AI is making an impact

  • “Hogan notes how, in many respects, eCommerce paved the way for AI. Data collected for things such as advertising helps to train neural networks. In addition, eCommerce firms have consistently aimed to improve and optimise their algorithms for things such as recommendations to attract customers.
    Nvidia has a machine called the DGX-2 which delivers two petaflops of performance. “That is one server that’s equivalent to 800 traditional servers in one box.”
  • Hogan says this has saved Walmart tens of billions of dollars. “This is just one example of how AI is making an impact today not just on the bottom line but also the overall performance of the business”.
  • Accenture is now detecting around 200 million cyber threats per day, claims Hogan. He notes how protecting against such a vast number of evolving threats is simply not possible without AI.”


Amazon Used An AI to Automatically Fire Low-Productivity Workers

  • “Documents obtained by The Verge show how Amazon used a computer system to automatically track and fire hundreds of fulfillment center employees between for failing to meet productivity quotas — a grim glimpse of a future in which AI is your boss.
  • But the automated tracking-and-firing system sounds even more egregious — placing power over employment in the hands of an AI that tracks invasive details like the amount of time employees spend ‘off task.'”


Blueshift raises $15 million from Softbank for its AI customer engagement tools

  • “There’s merit to Blueshift’s approach. A recent report published by Forrester Research found that highly personalized, omnichannel marketing campaigns have the potential to generate four times more revenue and 18 times greater profits than static campaigns.
  • In practice, Interaction Graph enables marketing managers to manage email, push notifications, text messages, social media accounts, and webpages in one place, and to A/B test and optimize content in an automated fashion. Through Blueshift’s Personalization Studio dashboard, admins can segment users in real time by behavior (i.e., attributes and time frame) and demographic (names and IP addresses), all while proprietary algorithms tabulate predictive scores to identify which users have a high or low likelihood of completing various actions.
  • ‘Marketers need a system that was built ground up with AI,’ said Storm Ventures managing director Tae Hea Nahm. ‘Savvy digital marketers are starting to realize that AI Marketing requires a fundamentally different architecture. Like the early transition to SaaS and now to AI, incumbents will have a very hard time re-architecting their platform.'”


What Is Contextual Commerce and Why It Can Be So Hard

  • ‘Contextual commerce is the idea that consumers should be able to buy whatever they want, wherever they want, with as few hoops to jump through as possible.’
  • On the back end, there are numerous systems a retail can harness to connect with consumers. “Any multivariate platform like Rich Relevance, Optimizely or Maxymiser can associate content with a shopper’s context,” said Chris Haines, director of consulting at Amplience. But there are challenges with contextual commerce, he continued, namely that none of the leading ecommerce platforms are built to handle content — or any non-product data for that matter.
  • Artificial intelligence also plays a significant role in the better executions of contextual commerce. “Almost all concepts in contextual commerce require AI to help detect important attributes about the context,” Finkelshtey said.”


How To Move Past Artificial Intelligence #Fails

  • “As documented by research into artificial intelligence by PYMNTS and Brighterion, among the main AI problems for financial institutions is lack of understanding. Executives and managers often confuse artificial intelligence (which is capable of unsupervised learning) with its less sophisticated but close cousin, machine learning (which is capable of supervised learning). In fact, as Webster and Adjaoute discussed, confusion about what AI really is can lead to delays in deployments. Some 15 percent of financial institutions that have not yet implemented AI (but want to do so) report difficulties getting buy-in from executives.
  • The larger promise of AI’s capabilities does seem to be getting out, however. The PYMNTS-Brighterion research shows that 41.1 percent of commercial banks are “very” or “extremely” interested in adopting smart agents. Not only that, but 45 percent of decision makers working in fraud detection are interested in adopting smart agents.
  • Lack of transparency also hinders deployment of artificial intelligence and can also be considered a source of failure – at least so far in the AI story as it relates to working within financial institutions. PYMNTS research found that 42 percent of FIs said the AI model is not transparent enough.”