It’s time to address the reproducibility crisis in AI

 

GUEST: Recently I interviewed Clare Gollnick, CTO of Terbium Labs, on the reproducibility crisis in science and its implications for data scientists. The podcast seemed to really resonate with listeners (judging by the number of comments we’ve received via the show notes page and Twitter), for several reasons. To sum up the issue: Many resear…Read More

Passive subscriber churn — and how to mitigate it (VB Live)


VB LIVE: Subscription businesses can lose the happiest of subscribers because of involuntary churn. But machine learning can automatically reduce this passive churn and boost monthly recurring revenue by an average of 9 percent. Learn more about how to improve transaction success rates and billing continuity when you join this VB Live event! Access…Read More

Getting Linked In to Data Science with Dr. Igor Perisic

Dr. Igor Perisic – Chief Data Officer

Episode 11, February 7, 2018

Getting Linked In to Data Science with Dr. Igor Perisic

Big data is a big deal, and if you follow the popular technical press, you’ll have heard all the metaphors: data is the new oil, the new bacon, the new currency, the new electricity. It’s even been called the new black. While data may not actually be any of these things, we can say this: in today’s networked world, data is increasingly valuable, and it is essential to research, both basic and applied. Continue reading “Getting Linked In to Data Science with Dr. Igor Perisic”

7 predictions for the evolution of enterprise AI in 2018

While artificial intelligence applications in business and industry remain limited to narrow machine learning tasks, we are seeing progressive improvements in the convergence of algorithms and hardware that will have significant implications for how well and how quickly we can implement AI. Researchers can now train neural networks within a few hours or days, which opens up an amazing range of possibilities, products, and things to learn — as well as challenges — that we could not have even considered before. Continue reading “7 predictions for the evolution of enterprise AI in 2018”

In a Confusing World, Context is Key — A Times Intern Sets Out to Improve Search Results

Illustration by Kevin Zweerink for The New York Times

The past few years have seen the rise of “context-aware” systems: technologies that can predict your intentions based on information about your environment. If you ask Google’s intelligent personal assistant, “How tall is that building?” it will use your phone’s GPS to see what buildings are near you and guess which building you are asking about. Or, if you add “pick up milk” to the Reminders app on your iPhone, you can choose to have the app remind you the next time you are within a block of a grocery store. Continue reading “In a Confusing World, Context is Key — A Times Intern Sets Out to Improve Search Results”

4 things I learned building chatbots for major brands in 2017

In 2017, my team powered chatbots and voice skills for leading brands like Nike, Vice, Jameson, Marriott Rewards, Simon, Gatorade, and more. We witnessed new user behaviors and uncovered an evolved set of best practices to build a chatbot. Here are four actionable learnings from our work that you should consider when launching your own chatbot in 2018.

1. Personalization drives engagement

Bots that are designed to segment and engage customers throughout the entire conversation drive higher metrics than chatbots that do not personalize the conversation. For example, in our testing, personalized results yielded the highest click-through to website, up to 74 percent in some cases.

This year, a leading athletic brand set out to inspire a sneaker style for girls across the globe. The brand launched a customized sneaker builder where the user uploads of a photo of her outfit, and magically, in an instant, the bot pulls up a pair of shoes that matches the uploaded picture. This experience drove a click-through rate 12.5X higher than the global brand average.

Bud Light launched a chatbot with the goal of driving demand and purchase of Bud Light’s team cans on game day throughout the NFL season. A personalized data model and chatbot powered the ordering and delivery of team cans every game day during the NFL season. The Bud Light chatbot acted as a utility to remind fans that it was game time, and to order Bud Light before the game. Bud Light saw an 83 percent engagement rate with personalization.

2. Get to the point quickly

Across multiple chatbots, about half of the first actions that users take is free text entry. Updating the onboarding copy to manage expectations — “this is a bot that can do X and Y,” for example — lowers that initial friction. If the first intent is help-related or a long-form text entry, you can provide a customer service number, FAQs, or an option to “talk to a human” from the very beginning.

When users get into the designed experience, point of sale should be within five clicks. For example, after A/B testing a chatbot across 250,000 users, we noticed a significant drop-off occured when the core focus (click to purchase, etc.) was beyond five clicks.

3. Chatbots go beyond mobile devices

Bots are an effective tool to drive real-world activities or offline conversions, with coupon redemption rates as high as 30 percent.

A leading quick-service restaurant brand launched a new bot that drove users through an immersive content experience with videos, quizzes, recipes, and coupons. This high engagement led to over 71,000 coupons redeemed from the chatbot.

The Jordan Brand aimed to reach elite high school football, basketball, and baseball athletes with an ongoing training chatbot experience for pre-season training. Jordan delivered nightly prep videos and daily workout series to a targeted group of high school athletes in advance of basketball season on Facebook Messenger. Athletes loved receiving push notifications reminding them to work out. Jordan saw an extremely high completion rate as well as a high re-engagement rate compared to regular customer relationship management programs: Over 70 percent of users surveyed enjoyed the experience.

4. Truly understand your users

Understanding why people did or did not enjoy the experience is key. One way to do this is using free text analysis to understand sentiment and drop-off. For example, we launched a new bot with a leading shoe retailer. Most people came to the bot knowing what specific shoe they wanted to buy or with a question about the shoe they already bought. Cater to the specific pain points and make sure your bot handles customer intent at every stage.

Finally, make sure to survey users and learn from both your best purchasers as well as your qualified no’s. One way to do this by asking your users directly. You can use a chatbot for net promoter score surveying.

Jonathan Shriftman is the director of business development at Snaps, a mobile messaging service.

2018 will be the year chatbot conversations get real

As we enter 2018, it’s clear the expectations of intelligent assistance providers and enterprise practitioners are more in balance than ever before. It’s also clear that the number of “known knowns” is growing rapidly.

Here are five examples of how and why we will learn more about the complex workings of conversational commerce in the coming year. Continue reading “2018 will be the year chatbot conversations get real”

4 predictions for conversational AI in 2018

As marketers look into 2018, they see the conversational AI landscape is primed for increased consumer adoption. In fact, in a recent survey, nine out of ten people said they prefer messaging directly with a brand. This year, Apple, Facebook, Google, and Amazon all lean-in to messaging and conversation. In 2018, the big four will make conversational AI the main gateway to communicate with the customer.

Consumers and brand marketers will see an uptick in the following areas: Continue reading “4 predictions for conversational AI in 2018”

How IBM builds an effective data science team

Data science is a team sport. This sentiment rings true not only with our experiences within IBM, but with our enterprise customers, who often ask us for advice on how to structure data science teams within their own organizations.

Before that can be done, however, it’s important to remember that the various skills required to execute a data science project are both rare and distinct. That means we need to make sure that each team member can focus on what he or she does best.

Consider this breakdown of a data science project, along with the skills required for each role:

Continue reading “How IBM builds an effective data science team”

5 tips to humanize your chatbot

Chatbots are an increasing part of our daily lives, redefining how we engage with the internet and with businesses. Canadian messaging company Kik explains it like this: “First there were websites, then there were apps. Now, there are bots.” Just like the early internet, bots are set to transform commerce as we know it, making it easier than ever for consumers to reach, engage, and transact through instant commands. Continue reading “5 tips to humanize your chatbot”

5 best practices for implementing voice marketing in 2018

Hey Alexa, play some music.
Ok, Google, turn on the lights.

Five years ago, these commands would have made no sense. But for the past two and a half years, voice-enabled speakers have steadily gained traction, introducing the world to voice-activated technologies. As we approach 2018, there’s no sign of slowing down the smart speaker revolution. Continue reading “5 best practices for implementing voice marketing in 2018”

AI might just live up to all the hype one day

If AI isn’t the most hyped technology of the 21st century, it’s certainly right up there with earlier manias for mobile, virtual reality, the internet of things, and big data. Companies large and small feel pressure to claim they use AI in some key way to drive their business. But does AI deserve this level of hype? On one end of the spectrum are the doomsayers (including heavyweights like Stephen Hawking and Elon Musk) who see the technology posing an existential threat to the future of humanity. In contrast, there are those who see AI as the breakthrough that could solve many of the world’s most intractable problems. Visionary Ray Kurzweil believes AI will soon enhance virtually everyone’s mental capabilities. Musk’s own startup, Neuralink, is reportedly developing a brain-to-machine interface that could improve memory or allow for more direct interfacing with computing devices. Continue reading “AI might just live up to all the hype one day”

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