MOL TOOL: “keeping Your talents engaged to Your business.” Since No one except Us, bothered to create Multilevel Operational Learning (MOL) #tools that prompts talents towards asking good #Questions , #Companies will spend considerable time in figuring out questions for #AI ; even joining #Expensive seminars outside of their industry for “question scoping”. more here:- https://searchenterpriseai.techtarget.com/news/252452490/An-AI-project-should-start-with-a-question-not-an-insight?track=NL-1816&ad=924240&src=924240&asrc=EM_NLN_103538717&utm_medium=EM&utm_source=NLN&utm_campaign=20181115_Microsoft%20gives%20Power%20BI%20more%20enterprise%20BI%20features;%20AI%20lessons%20learned%20from%20Gartner;%20SQL%20Server%20security%20best%20practices
We’ve invented MOL or Multilevel Operational Learning tool to help train and convert IT guys into Analytics. This in return helps your AI Training data close to general AI Intelligence. Analytics now is not about lack of or presence of Data Scientists. It is about diversity of experiences who are trained in analytics and contribute to AI training data.
Most companies struggle with employee churn and engagement. Since 2000, 52% of #fortune 500 companies have vanished or good #talent solution makes companies 52% more profitable. Since continuous learning talent is required for digital age, hunt for talent is on! #AI hype has created enormous demand for human labor; sprouting upstream #Companies such as data cleaning and now – data labeling for downstream Analytics. Many companies jumped into #AI #hype without realizing huge scale of #talent needed. more here:- https://venturebeat.com/2018/11/16/hive-taps-a-workforce-of-700000-people-to-label-data-and-train-ai-models/?utm_source=VentureBeat&utm_campaign=401263088b-VB_Daily&utm_medium=email&utm_term=0_89d8059242-401263088b-33454215 . Don’t just start with use case for #AI ; start with #business case. That means asking lots of good questions and scoping at least two to three business cases from those #Questions . more here:- https://searchenterpriseai.techtarget.com/feature/Lessons-learned-from-3-AI-use-cases?track=NL-1816&ad=924240&src=924240&asrc=EM_NLN_103538714&utm_medium=EM&utm_source=NLN&utm_campaign=20181115_Microsoft%20gives%20Power%20BI%20more%20enterprise%20BI%20features;%20AI%20lessons%20learned%20from%20Gartner;%20SQL%20Server%20security%20best%20practices
Working with data makes learning fast because it starts working on curiosity inside you. Curiosity is number currency of talent but 95% companies globally don’t know how to hire and train for industry business knowledge. Bay Area has incredible hardcore IT Boot-camps! For talent with fresh undergrad and graduate quantitative degrees, there is no tool globally to find their curiosity. How will they perform in jobs within two weeks on self learning tool and self-creating questions and hypotheses? Answer helps company as much as talent. There was always a need for a tool that technical and business talent can use to learn because there is a need to figure out data sets. There is a need to turn AI black-box to bright-box. And they require Curiosity! We invented Multilevel Operational Learning (MOL) Tool after 6 years of Algorithm research that offers summarization, visualization – questions comes to you as next step, not the other way around. Our MOL tool enhances curiosity, business knowledge of at levels and hence Engagement of talent regardless of industry experiences.
Talent in global Corporate Environment is looking for critical thinking and problem skills and Universities globally have responded by MBA- Analytics like programs. In USA, while admission in all other programs is at 2.4%, in Analytics related education enjoys 7.4% admission rate with very high tuition fees. Unfortunately, they are more of MBA Lite programs – not much Actionable Learning. So No one except Us, researched with machine Learning algorithms and thought to create a tool that addresses talent’s needs.
Almost two hours of lecture at Golden gate university on 12/11/2017 in San Francisco by founder, Navin Sinha, then was helpful to students. Reflecting back, students must be taught healthcare business in USA universities. They need to know, CPT, HCPC, Modifiers etc. We also reflected on this experience and found weakness in our MOL or Multivariate Operational learning tool since 12/11/2017; developed many Machine Learning algorithms to find if the talent is curious, then help such talents ask smart questions, create testable hypotheses, learn healthcare fast and forget about next employer(s).