Collaborating Authors

Information Management

ML Scaling Requires Upgraded Data Management Plan


Successful data strategies are built on a foundation of meticulous data management, creating enterprise architectures that "democratize" data access and usage, yielding measurable results from machine learning platforms. The reality, according to an examination of the emerging "AI organization," is that few data-driven organizations are able to deliver on their data strategy. A survey commissioned by Databricks and conducted by MIT Technology Review Insights found that a mere 13 percent of those polled actually achieve measurable business results. MIT Technology Review Insights said it polled 351 CDOs, chief analytics officers as well as CIOs, CTOs and senior technology executives. It also interviewed several other senior technology leaders.

AI in Content Marketing


AI tools can transform the entire marketing production process, helping marketing teams make data-driven decisions about what to write, who to write for, and how to reach readers as effectively as possible. Tools like Smart Compose from Gmail are already producing short-form content that replicates a human tone. Does this mean that robots will be writing the content you enjoy reading every day? Currently, AI is being used primarily in content production planning stages. For example, tools that can dynamically cluster relevant content topics can help marketers identify actionable opportunities. Some tools help marketers navigate changes occurring in search engines and social media algorithms.

Building a high-performance data and AI organization

MIT Technology Review

In this context, effective data management is one of the foundations of a data-driven organization. But managing data in an enterprise is highly complex. As new data technologies come on stream, the burden of legacy systems and data silos grows, unless they can be integrated or ring-fenced. Fragmentation of architecture is a headache for many a chief data officer (CDO), due not just to silos but also to the variety of on-premise and cloud-based tools many organizations use. Along with poor data quality, these issues combine to deprive organizations' data platforms--and the machine learning and analytics models they support--of the speed and scale needed to deliver the desired business results.

Weaviate is an open-source search engine powered by ML, vectors, graphs, and GraphQL


Bob van Luijt's career in technology started at age 15, building websites to help people sell toothbrushes online. Not many 15 year-olds do that. Apparently, this gave van Luijt enough of a head start to arrive at the confluence of technology trends today. Van Luijt went on to study arts but ended up working full time in technology anyway. In 2015, when Google introduced its RankBrain algorithm, the quality of search results jumped up.

Data Modeling Mastery for AI and Beyond


An inordinate amount of some of the most vital aspects of Artificial Intelligence--from data engineering to data science, data preparation to machine learning--rely on one indispensable prerequisite: data modeling. Without effective data modeling, organizations can't integrate data across sources to build advanced analytics models. Data modeling is foundational to assembling training datasets, utilizing specific data for end user applications, and scaffolding predictive cognitive computing models. Consequently, it behooves companies to make the modeling process as efficient as possible to achieve the following three benefits that optimize their modeling endeavors--and the advanced analytics applications and use cases they support. These advantages are difficult, if not impossible, to realize with traditional relational approaches to data modeling.

Searching symptoms online helps patients make a good diagnosis, doesn't increase anxiety, study shows

Boston Herald

Google" and researching health issues online makes patients better at diagnosing illnesses and doesn't make them more anxious, a new study out of Harvard and Brigham and Women's Hospital shows. "Every doctor has their story about the patient who has pinky pain who thought they had cancer," said Dr. David Levine, corresponding author of the study and internist at Brigham and Women's. But Levine said that's certainly not the norm, and he loves when his patients Google their symptoms before arriving at his office, "I think it shows they're invested in what's going on." Levine and his colleagues found that study participants showed modest improvements in reaching an accurate diagnosis after looking up symptoms online and reported no increase in "cyberchondria," or anxiety about one's health associated with using the internet. Googling health symptoms has often been thought of as a no-no due to online misinformation or the potential to stoke fear in patients, but Levine said the research findings show that's not quite true.

How to Get AI to the Edge - The New Stack


Digital transformations are being fuel-injected by leading enterprise tech like hybrid clouds, containers, and AI. Adding to the acceleration is 5G, which is adding decentralized data and application processing from millions of endpoints outside the traditional datacenter and public cloud. But while transformation and modernization are bringing improved enterprise performance, efficiency, and agility, these increasingly complicated infrastructures are also complicating data management and availability, especially for AI workloads. For example, capturing data from the edge of the network, not to mention exogenous data from external sources, usually means moving and copying data -- a process that is not only time consuming and expensive, but also introduces new levels of risk, governance, and security challenges. Today, one of the few ways around this challenge is to flip the equation and push the AI to the data, rather than the other way around.

10 Applications of Artificial Intelligence in Digital Marketing


Artificial Intelligence has marked its presence in almost every industry and walks of life. It has not only been reducing the human interventions in various operations but also helping humans to do their job better. Fields like Social Media, Consumer Electronics, Robotics, Travel and Transportation, Finance, Healthcare, Security, Surveillance, E-commerce, etc. are already benefiting from AI. Digital Marketing and AI go hand-in-hand. In digital marketing, there is a massive requirement to process tons of data. Artificial Intelligence helps digital marketers to process data faster, which allows them to create digital strategies more efficiently.

Value Insights Data Specialist/Sr. Specialist


Gong enables revenue teams to realize their fullest potential by unveiling their customer reality. The patented Gong Revenue Intelligence Platform captures and understands every customer interaction, then delivers insights at scale, empowering revenue teams to make decisions based on data instead of opinions. Over 1,500 innovative companies like Zillow, Slack, PayPal, Twilio, Shopify, Hubspot, SproutSocial, Zoominfo, Outreach, MuleSoft, and LinkedIn trust Gong to power their customer reality. With Gong, customers experience improved win rates, increased deal sizes, and accelerated employee ramp-times. Gong's Value Engineering team is responsible for helping Gong's customers measure the benefits of, and drive more value out of the Gong technology, using data.