In this episode Steve Zakur and I are curious about the ways AI can be used to drive greater value for your company. We have our opinions about our own software of course, but this is a bigger question: how can you use AI to make your entire team and business smarter? What companies need to think about right now is AI augmentation -- augmenting decision making. Sometimes we're thinking way too big about AI, instead of in a targeted fashion about what it can do for us now. This is the importance of practical AI.
Managing stakeholders in the world of data science projects is a tricky prospect. I have seen a lot of executives and professionals get swept up in the hype around data science without properly understanding what a full-blown project entails. And I don't say this lightly – my career has been at the very cusp of machine learning and delivery. I hold a Ph.D. in Data Science and Machine Learning from one of the best institutions in the world and have several years of experience working with some of the top industry research labs. I moved to Yodlee, a FinTech organization, in 2016 to run the data sciences product delivery division.
When we think SEO, Google is the next thought most of the time, right? Pleasing the "Google Gods" gets trickier as technology evolves. The art of staying visible on the web is always changing, and unless you're an SEO specialist, it can be tough to stay on top of the ever-evolving trends. There is so much global competition. And web user attention spans are perilously short.
That's a question I got recently, and it got Steve Zakur and I wondering where we are on the hype curve. Is it really just all hype? Maybe not -- platform companies are snapping up data scientists the way early internet companies snapped up web developers. The real question might be "is there value in AI?" And that really comes down to what problems you want to solve, and what data you have to solve them.
Monsters and titans share the stage of mythology across cultures as the necessary realizations of the human imagination. From stone cave to urban dwelling, the theme is unremitting; kept in the imagination, such creatures perform, innocently enough, benign functions. The catch here is the human tendency to realize such creatures. They take the form of social engineering and utopia. Folly bound, such projects and ventures wind up corrupting and degrading.
Machine learning uses data to help predict outcomes presenting usable analytics that help prime marketers to succeed. That is the simplest way to explain it. For marketers, this is the main driving force behind things such as Facebook newsfeed ads and chatbots. It has already made an impact on how data is used to effectively improve the customer experience. This means businesses can find deeper knowledge from consumer data to greatly improve marketing processes.
This is a sponsored post written by Atomic Reach. The opinions expressed in this article are the sponsor's own. When it comes to shopping, many customers have decided to take their business online. Statista has estimated that 1.92 billion global buyers will participate in ecommerce activities in 2019. The number is expected to rise to more than 2 billion by 2021.
At first glance, the Berlin startup doesn't seem so different from others: a factory floor in the rear courtyard of a building in the city's Neukölln district, stacked preserving jars filled with muesli in the kitchen, a discarded ping-pong surface repurposed as a conference table. The employees are young, relaxed and very international. The company's head and founder, Christian Kroll, is 35 years old, the same age as Mark Zuckerberg. The two men also share a quirk: To avoid wasting time in the mornings choosing an outfit, he always wears the same thing -- in his case, blank white T-shirts made from organic cotton. Zuckerberg's favorite color, by contrast, is gray.
A disconnected/disengaged workforce, broken business processes and an overall decrease in efficiency represent the most recurrent challenges facing organizations today. As a result, digital workplace solutions have grown in popularity as they offer an holistic solution capable of integrating different tools and applications. A typical digital workplace includes a knowledge management system (KMS), an enterprise social network (ESN), an intranet portal, instant messaging and more. It also integrates different third party software used internally, from CRM to Human Resources Information Systems (HRIS). For better usage and efficiency, a digital workplace needs to collect data from all these data sources and make it widely accessible to users in a centralized place – thus the importance of the enterprise search engine.
At Etsy, the search challenge is particularly tough. The site's stock in trade is not the sort of mass-produced goods that can be neatly categorized. Instead, 75% of the 60 million items that its 2 million merchants offer are handmade and therefore one of a kind. Even if they speak deeply to a shopper, they may do so for reasons that are difficult to divine from search terms and the information in product listings. "We don't have merchandisers entering the descriptions of the blue shirts in the pallets in the warehouse," says Mike Fisher, Etsy's CTO.