The Deceptive Arguments Amazon Uses to Shirk Responsibility for AI
In a recent column, Recode founder and New York Times columnist Kara Swisher cut to the core of what would seem to be concessionary calls for regulation from Big Tech firms, summarizing their attitude like this: “We make, we break, you fix.” She’s right, and with Google, Amazon, Apple, and Facebook doubling their combined lobbying spending from 2016 to $55 million in 2018, it is worth taking a closer look at the kinds of arguments the companies are trotting out to avoid responsibility for the outcomes of the technology they produce and sell. We should be particularly concerned about the arguments tech firms are making about AI, which is already remaking our society, replacing steps in crucial human decision-making processes with machine-generated solutions.
For an example of how tech firms are attempting to get away with peddling potentially dangerous AI-based tech to powerful entities like law enforcement agencies while accepting minimal accountability, consider Amazon’s Rekognition.
July Focus: Retail Transformation in the Amazon Age
Street Fight is rolling into July with the monthly theme Disrupting Retail: a look at how retail continues to transform, driven by competition from Amazon and key trends like “retail-as-a-service.”
But why is this important to Street Fight (and to you)? As we continue to evolve the definition of “local,” one key component of its market opportunity is offline brick-and-mortar shopping. After all, about 90% of all U.S. retail spending, to the tune of about $3.7 trillion, is completed offline in physical stores. And that’s usually in proximity to one’s home (thus, local).
Consumers Welcome Some Automated Business Messaging, But Humans Must Tag Along to Help
More than half of consumers are frustrated by customer-service situations in which they can only interact with automated agents, and nearly one in five even reporting feeling angry in those situations. That’s per a new survey of U.S. consumers conducted by The Harris Poll and commissioned by call tracking and analytics firm Invoca.
Heard on the Street, Episode 28: Location-Based Survival of the Fittest, With Gimbal
According to Gimbal’s SVP of location platforms Adrian Tompsett, the key to the location business is having a long-term and holistic view of customer value. That means using location intelligence to go beyond just triggering promotions to increase the customers’ basket size, instead using the technology in ways that will provide additional value in the long term.
How to Use Facebook’s Simplified Campaign Structure to Your Advantage
It’s a brave new advertising world. The algorithms are taking over, whether human advertising managers like it or not. Our best bet is to understand how the algorithms work and to give them the freedom, the data, the budgets, and the creative assets they need for optimal performance. The Facebook algorithm will take away budget lever from humans when Campaign Budget Optimization becomes mandatory in September 2019.
How Emerging Technologies Allow Businesses to Merge Their Digital and Traditional Marketing
New technologies (and new spins on old ones) are the modern company’s ally in merging digital and traditional marketing. The brands that find a sensible balance between the two are the brands that will outperform the competition. Let’s take a look at four major examples of innovation in this arena.
Brands See Risk and Reward When Automating Reputation Management
With the reviews and other content being posted online about brands coming from an increasingly wide swath of sources, manual techniques for reputation management are no longer viable on a large scale. At the same time, the volume of online opinions bombarding potential customers is making it more important than ever for brands to constantly monitor what’s being said about them online. How are brands coping with the challenge?
AI Is No Magic Bullet for Policing Hateful Content
The task Facebook must take up as it attempts to police hateful content is one inseparable from political values, human judgment, and the interpretation of statements that need to be parsed by well-trained eyes and bright minds with a stomach for horror to boot. While machines will play an indispensable role in content moderation on a platform of Facebook’s scale, they will be far from sufficient. That’s because monitoring hate speech touches on nothing less than some of humanistic inquiry’s age-old questions: the debatable violence, status of truth, and foundations of meaning in language.
Things Not Strings: Google’s New Hotel Profiles Exemplify Its Approach to Entities
Google’s Knowledge Graph ambitions are expanding to include obviating heavy reliance on secondary sources like Wikipedia and being able instead to classify and cross-reference information as a native, self-sustaining activity on web pages themselves. That’s what makes a recent patent filing different from the evidence of the Knowledge Graph we’ve already seen in the wild.
While this more ambitious way of surfacing information about entities is not yet standard, in researching Google’s new interface for hotels, I think I’m seeing evidence of a real-world example.
Heard on the Street, Episode 23: Google, AI, 5G and Marketing Champagne
What do Google’s AI-fueled search results, 5G, and marketing champagne all have in common? They’re the central topics of a roundtable discussion on the latest episode of Street Fight’s podcast, Heard on the Street.
As we do quarterly, this is a bonus episode that puts aside our typical interview format and instead invites the leading thinkers from the Street Fight newsroom and executive ranks to discuss news and insights that are top of mind.
Human Judgment, Automation, and the Future of Ad Tech
For now, I propose two major concerns—two challenges, even, for further thought—surrounding AI for the ad tech industry. The first is that the datafication of human experience that has allowed for precise ad targeting needs to be radically reconsidered, not just in terms of what can be done to obtain the consent of consumers for data collection, as the rising privacy movement has called tech companies to consider, but also in terms of what is lost and what is truly gained when the attributes of real people are transformed into consumer data. The second is that the human-machine hybrid decision-making model, while surely the best available in a hypothetical set that also includes human-only and machine-only models, will have to grapple with the bias and poor decisions of the humans who program the machines that will take on the task of regulating large platforms at scale.
AI Technology is Getting within Reach for Small Restaurants
Rodion Yeroshek: The majority of restaurant businesses, especially the small ones, remain slow adopters and non-adopters of AI technology. People may think that the introduction of AI in small restaurant operations is nothing more than jumping on the bandwagon. However, research on the impact of AI on the world economy by McKinsey Global Institute warns the naysayers. The research predicts that by 2030 active adopters of AI technologies could double their cash flow, while non-adopters could lose up to 20% of theirs. This is a hint for restaurant managers who plan to stay in business for the next 10-15 years that it’s time they embrace AI tools or prepare to lose a big part of their market share for good.
7 Delivery Trends You Should Know in 2019
Greater customer expectations and technological advancements are driving big changes in delivery. What’s more, the delivery experience has emerged as a differentiating factor for customers when choosing one retailer over another. eCommerce retailers that operate solely online and omnichannel retailers that offer a physical and digital presence are both beginning to expand their delivery options to meet customer demand. Here are seven trends that will define retail delivery during 2019.
5 Platforms Using AI to Analyze Customer Reviews
Beyond the star ratings lies a wealth of information. Sentiment and opinions can be used to shape the way brands develop their highest-selling products. Given the volume of reviews posted each day, however, it would be impossible for most major brands to analyze every customer reaction individually. Instead, a growing number of brands are utilizing artificial intelligence (AI) technology to extract and analyze the sentiment from product reviews. Here are five examples of platforms that offer this type of AI technology for analyzing customer feedback posted online.
The Future of AI Is Here: Reflections on IBM Think
Damian Rollison: Among hundreds of sessions, exhibits, and demos, one theme came through clearly at IBM Think this month in San Francisco: for large enterprises especially, the AI-driven future for which we’ve been told to prepare is already here. In fact, enterprise companies are using IBM’s Watson technologies today to address a myriad of challenges inherent in the scale of those businesses.
Adjust Leverages AI to Neutralize Ad Fraud
Advertisers and brands are expected to lose an estimated $50 billion as a result of ad fraud by 2025, with one of the most problematic types of ad fraud involving bots designed to mimic human behaviors. Using bots, fraudsters can imitate clicks and engagement KPIs on ad campaigns, wreaking havoc for mobile ad vendors and the advertisers that work with them. So what’s the solution? Firms like Unbotify are pioneering a new approach to bot detection and digital fraud prevention using artificial intelligence and machine learning. Unbotify’s solution analyzes human behavior patterns within websites’ and mobile apps’ user flows in order to differentiate between bots and humans.
Data Science as a Solution: conDati Opens Doors for Digital Retailers
The pitch is that today’s marketers with omnichannel inspirations need a machine learning-driven platform that will not only assess the success of campaigns across several media but also point them toward paths for future success. That’s an expensive technical infrastructure to create in-house, and conDati’s betting its solution is worth the spend.
Data Trends with the Highest Impact In 2019
At the beginning of the year, we like to take time and speculate on which data science trends will make the biggest splash in the year. Now that we’re entering the second half of 2019, it is a good time to take a look at our initial assumptions regarding these trends and re-evaluate each one’s impact on the industry.