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The State of AI and Machine Learning

In the 7th edition of its annual State of AI report, Appen continues to explore the strategies  employed by companies large and small in successfully deploying AI. The reports surveys business  leaders and technical practitioners ( referred to as technologists) alike to understand  their priorities, their successes, and their bottlenecks when it comes to implementing AI.  Collectively, their answers enabled the company to paint a picture of how the AI industry continues to  evolve in a world that is more virtual, more tech-savvy, and more globalized than ever.

insideBIGDATA Guide to Big Data for Finance

The goal for this insideBIGDATA technology guide, sponsored by Dell Technologies, is to provide direction for enterprise  thought leaders on ways of leveraging big `data technologies in support of analytics  proficiencies designed to work more independently and effectively across a few distinct areas in today’s financial service institutions (FSI) climate: (i) Retail Banking; (ii) Regulatory and Compliance, (iii) Algorithmic Trading; and (iv) Security Considerations.

Reduce Compliance Risk in Your Call and Contact Center With AI

This white paper from Veritone discusses how compliance represents a major risk for contact centers, with violations potentially costing organizations millions of dollars in fines and causing reputational damage to the brand. The current method of monitoring calls — using manual review of recorded conversations — does not come close to meeting the requirements for comprehensive monitoring of regulated interactions. AI text-to-speech technology provides the solution to this dilemma, with its capability to transcribe, translate, redact, find objects and generate searchable indexes of limitless hours of recorded calls in near real-time.

Data Engineering Survey: 2021 Impact Report

This Data Engineering Survey: 2021 Impact Report summarizes key findings from the inaugural survey and provides a glimpse into the current and future state of data engineering and DataOps. The report highlights some of the major trends uncovered in this year’s survey including the adoption of cloud data platforms, what platforms are winning (and emerging), what data engineers find to be their biggest challenges, and how organizations are handling sensitive data.

XAI: Are We Looking Before We Leap?

This four part report from our friends over at SOSA highlights the challenges and advances when it comes to regulations, relevant use-cases, and emerging technologies taking the mystery out of AI.

Driving ROI Through AI

This new report from ESI ThoughtLab was conducted alongside our friends over at DataRobot as well as a coalition of other AI leaders. The report shows that despite high adoption rates of AI in enterprises, ROI in AI still remains a work in progress and will take skill, scale, and time.

How big data works? How we use it at FunCorp, and why it’s one of our most important tools

The latest report on big data from FunCorp discusses how an increased usage in smartphone users has also led to a rising demand for better mobile apps. These modern apps also use tremendous amounts of data, and thus, a robust management tool for analyzing and managing this data has become a necessity. This is where the use of Big Data comes in (this includes FunCorp too). The company develops apps and has relied heavily on big data. Read this report to learn how.

Be (More) Wrong Faster – Dumbing Down Artificial Intelligence with Bad Data

In this white paper, our friends over at Profisee discuss how Master Data Management (MDM) will put your organization on the fast track to automating processes and decisions while minimizing resource requirements, while simultaneously eliminating the risks associated with feeding AI and ML data that is not fully trusted. In turn, your digital business transformation will be accelerated and your competitive edge will be rock solid.

The Real AI Revolution: Machines That Learn Like Scientists

In this compelling white paper, our friends over at causaLens highlight how ML has wrongly become synonymous with AI. We must shake off this misconception to start  the real AI revolution. Data science must forgo its reliance on curve-fitting ML and return to its roots; to put the science back into data science. causaLens is a major contributor to this new science of causality. And it is the company’s mission to help  organizations of all types to benefit from it.

The Future Starts Now – Achieving Successful Operation of ML & AI-Driven Applications

Operationalizing AI and ML has become an unavoidable need in business, as various industries heavily rely on large volumes of real-time data as input to automated decision-making processes to yield the best results. Use cases in the data science field have shown that ML models and AI have few tangible business benefits until they are operationalized. In this e-book, our friends over at MemSQL show us how to successfully deploy model-driven applications into production.