Artificial Intelligence/Machine Learning (AI/ML) and Learning Systems in the Brain

Simulating Learning Processes in the Brain With AI/ML

Key Stakeholders: Chief Learning Officers, Chief Human Resource Officers, Learning and Development Directors and Managers, Corporate Trainers, Content and Learning Product Managers.

Why It Matters: The skills necessary for success in the corporate world are varied and include hard skills, people skills and situational awareness. While L&D is embracing the use of AI/ML to analyze learners’ data and to personalize learning paths, curate effective content, and attempt to better engage learners, what L&D has failed to embrace is the application of AI/ML to model each of these distinct learning systems, and their interactions.

Top Takeaway: Corporate learning vendors would be well served to develop AI/ML models that capture the processing characteristics of the three learning systems in the brain known to mediate hard skills, soft skills, and situational awareness learning. A comprehensive AI/ML model that captured the processing characteristics of each of these three distinct learning systems could be used to develop and test products and tools that optimize content curation, learning paths, engagement, and delivery processes that will differ substantially across systems and tasks to be learned.

Vendors with the Skillset and Expertise to Build this AI/ML Tool: Cornerstone, CrossKnowledge, IBM, Infor, LTG, Oracle, Saba, Salesforce, SAP, Workday, and likely many others.

Artificial Intelligence/Machine Learning and L&D

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Todd Maddox Ph.D.’s Top Four Scientific Observations on DevLearn 2018

If you have a passion for learning then DevLearn is for you. DevLearn 2018 was quite the event. With excellent keynote addresses, breakout sessions, numerous vendors and great demos it was action-packed. I enjoyed every minute of DevLearn 2018 and I am already looking forward to 2019.

I took a few days to gather my notes and thoughts, and I have a number of observations on DevLearn 2018. I am sure that others who attended DevLearn 2018 will highlight different topics, and acknowledging that I was only able to speak in detail with a dozen or so vendors, here are my Top Four Scientific Observations.

Whether Talent, Behavioral or Data……The Impact of Science Continues to Grow

Relevant Vendors That I Spoke With: Adobe, Allego, EdCast, Inkling, iSpring, Learning Tribes, LEO Learning, MPS Interactive, Mursion, OttoLearn, Rehearsal, Schoox, STRIVR, Valamis

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Why It Matters that IBM Announced Trust and Transparency Capabilities for AI


Note: This blog is a followup to Amalgam Insights’ visit to the “Change the Game” event held by IBM in New York City.

On September 19th, IBM announced its launch of a portfolio of AI trust and transparency capabilities. This announcement got Amalgam Insight’s attention because of IBM’s relevance and focus in the enterprise AI market throughout this decade.  To understand why IBM’s specific launch matters, take a step back in considering IBM’s considerable role in building out the current state of the enterprise AI market.

IBM AI in Context

Since IBM’s public launch of IBM Watson on Jeopardy! in 2011, IBM has been a market leader in enterprise artificial intelligence and spent billions of dollars in establishing both IBM Watson and AI. This has been a challenging path to travel as IBM has had to balance this market-leading innovation with the financial demands of supporting a company that brought in $107 billion in revenue in 2011 and has since seen this number shrink by almost 30%.

In addition, IBM had to balance its role as an enterprise technology company focused on the world’s largest workloads and IT challenges with launching an emerging product better suited for highly innovative startups and experimental enterprises. And IBM also faced the “cloudification” of enterprise IT in general, where the traditional top-down purchase of multi-million dollar IT portfolios is being replaced by piecemeal and business-driven purchases and consumption of best-in-breed technologies.

Seven years later, the jury is still out on how AI will ultimately end up transforming enterprises. What we do know is that a variety of branches of AI are emerging, including

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Learning Elastic’s Machine Learning Story at Elastic{ON} in Boston

Why is a Data Science and Machine Learning Analyst at Elastic’s road show when they’re best known for search? In early September, Amalgam Insights attended Elastic{ON} in Boston, MA. Prior to the show, my understanding of Elastic was that they were primarily a search engine company. Still, the inclusion of a deep dive into machine…

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Oracle GraphPipe: Expediting and Standardizing Model Deployment and Querying

On August 15, 2018, Oracle announced the availability of GraphPipe, a network protocol designed to transmit machine learning data between remote processes in a standardized manner, with the goal of simplifying the machine learning model deployment process. The spec is now available on Oracle’s GitHub, along with clients and servers that have implemented the spec for Python and Go (with a Java client soon to come); and a TensorFlow plugin that allows remote models to be included inside TensorFlow graphs.

Oracle’s goal with GraphPipe is to standardize the process of model deployment regardless of the frameworks utilized in the model creation stage.

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Data Science Platforms News Roundup, July 2018

On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include: Alteryx, Anaconda, Cloudera, Databricks, Dataiku, DataRobotDatawatch, Domino, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta.

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Google BigQuery ML Extends the Power of (Some) Modeling to Data Analysts

Last week at Google Next ‘18, Google announced a new beta capability in their BigQuery cloud data warehouse: BigQuery ML, which lets data analysts apply simple machine learning models to data residing in BigQuery data warehouses.

Data analysts know databases and SQL, but generally don’t have a lot of experience in building machine learning models using Python or R. An additional issue is the expense, time-consumption, and possible regulatory violations of moving data out of storage in order to send it through machine learning models. BigQuery ML aims to address these problems by letting data analysts push data through linear regression models (to predict a numeric value) or binary logistic regression models (to classify a value into one of two categories, such as “high” or “low”), using simple extensions of SQL on Google databases, run in place.

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EPM at a Crossroads Part 1: Why Is EPM So Confusing?

This blog is the first of a multi-blog series explaining the challenges of Enterprise Performance Management aka Financial Performance Management, Business Performance Management, Corporate Performance Management, Financial Planning and Analysis, and Planning, Budgeting, and Forecasting. Frankly, this list of names alone really helps explain a lot of the confusion. But one of the strangest aspects…

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DBAs, Update Your Resumes! Oracle Announces the Availability of Oracle Autonomous Data Warehouse Cloud

On March 27th, Oracle announced availability of the Oracle Autonomous Data Warehouse Cloud, a service that will spin up a data warehouse and provide automated security, high availability, performance tuning, scaling, patching, and administration at a cost guaranteeed to be half of equivalent Amazon Web Services resources through May 2019. Built on Oracle Database 18c, this new service is both a godsend and a warning call for IT.

As Amalgam said last December, Oracle’s push towards what they are calling the “Autonomous Database” and “Autonomous Cloud” is an important step forward in envisioning an new generation of IT where the operational tasks of rules-based administration, monitoring, and iterative performance tuning are handled without direct human intervention. This will allow IT departments to drive more infrastructure into the cloud and reduce the overall Total Cost of Ownership. This is a fundamental change and differs radically from cloud providers such as Amazon and Microsoft that are providing granular services, but are not replacing the management of those services.

Here’s what you should expect

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Anaplan States Planning Is Dead, Focuses on the Era of Real-Time Decision


Recommended Reading for: Finance, Sales Operations, Supply Chain Management, IT Management, and Enterprise Strategy Personnel
Companies Mentioned: Anaplan, IBM, SAP, Oracle, Microstrategy, Tableau, DataRobot, TROVE Data, Louis Vuitton, Premji Invest, Salesforce Ventures, Top Tier Capital Partners, Baillie Gifford, Granite Ventures, Industry Ventures, Meritech Capital, Constellation Research, Ventana Research, IDC, Mint Jutras, ISG, Gartner, Apps Run the World, TechVentive

On March 6th and 7th, 2018, Amalgam Insights attended Anaplan Hub 18. Anaplan has been on Amalgam analysts’ radar for several years, as we consider Anaplan’s Hyperblock foundation and ability to serve multi-departmental planning in enterprises without a year or more of setup to be fundamental advantages. As we have covered this company, we have been waiting for Anaplan to reach its breakthrough moment where it takes its place as one of the true market leaders in enterprise applications. It is in this context that we attended Anaplan Hub and judged our interactions with Anaplan executives, customers, and partners.

This report provides updates on Anaplan’s key business metrics, executive insights from an analyst-only panel, keynote and product announcements, a 2018 perspective on customer success stories with Anaplan, and Amalgam’s expectations for Anaplan in 2018 and beyond as both a real-time planning application and a Platform as a Service.

Anaplan Key Business Updates

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