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AI-enabled enterprise starts with education, not tech

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The AI-enabled enterprise won’t be built in a day. Take it from representatives at companies knee-deep in building…

AI hardware, software and services for their customers and clients, including IBM, Affectiva Inc. and Grant Thornton LLP.

At the recent AI and the Future of Work event hosted by MIT, these representatives provided advice on how CIOs can start to build the AI-enabled enterprise — as quickly as tomorrow morning. One of the first steps they suggested CIOs take? Get caught up on what the AI terrain looks like.

“The number one thing I would say is to invest the time to really understand what is happening in AI,” said Nichole Jordan, managing partner of markets, clients and industry at accounting and advisory firm Grant Thornton.

AI literacy is a must

Jordan pointed to AI Magazine and O’Reilly Media’s artificial intelligence newsletter as two “simple examples” of how CIOs can incorporate AI education into their daily routines and that of their teams. She described this as just “a sprinkling,” but said the reading material can encourage discussions about artificial intelligence and how its resurgence might affect the future of the company.

Reading up on AI could be worthwhile even for the smallest organizations, according to Jordan. “It no longer requires a multimillion-dollar budget to get AI started in your organization,” she said.

AI, Grant ThorntonNichole Jordan

Take mergers and acquisitions, which require advisors to monitor and analyze disparate and often siloed data sources such as patent filings or regulatory findings. Today, AI is doing that kind of work and even collecting metrics on company culture, customer feedback and employee engagement that it scrapes from sites such as Glassdoor.

“Over time, the AI is able to develop and monitor trends, patterns, make recommendations to you for potentially other companies to put into your acquisitions portfolio,” Jordan said. “It is about speed and accuracy and being able to analyze a lot of data that we didn’t historically have the opportunity to bring together into one place.”

Knowledge overhype

Affectiva’s Gabi Zijderveld echoed Jordan’s remarks, saying that education is a must.

“There’s so much hype and fluff around AI because every bit of technology today is [marketed as] AI,” said Zijderveld, chief marketing officer and head of product strategy at the emotion measurement company.

As CIOs familiarize themselves with what’s out there, they also need to get a grip on the appropriate opportunities AI can provide to their companies, according to Zijderveld. In Affectiva’s case, its first customers came from an obvious market segment.

affective, AI, emotion AIGabi Zijderveld

Media and advertising companies began using the emotion AI technology, which can interpret facial expressions in real time, to test their content and assess audience response. These days, customers include educators who use the technology to help children with autism decode facial expressions, as well as medical care workers who can use it to detect Parkinson’s disease or as a benchmark for facial reconstruction surgery.

Zijderveld also suggested CIOs look at industry best practices, talk to their peers, find out what competitors are doing and uncover good examples of applied AI, taking note of their results and the products and technologies that drove those results.

And she provided a note of caution for CIOs: Don’t fall into the over-engineering trap. “If you have an old-fashioned ruler that does the job, maybe you don’t need AI there,” she said. “Use the damn ruler.”

Lifelong learning is key

For Sophie Vandebroek, vice president of emerging technology partnerships at IBM, building the AI-enabled enterprise means developing employee skills.

“At IBM, in fact, we are being measured to make sure we take 40 hours of education every year on these kinds of topics,” she said.

Not only is training important, but hiring and bringing in the right skills is also key, according to Vandebroek. For AI-enabled enterprises to succeed, employees who know how to use AI tools, especially as they become more accessible, easier to use and embedded into workflows, will be critical.

Vandebroek cited IBM’s Project Debater product as an example of how AI could change workflows. The AI system has been trained to take a topic, craft an argument and debate its merits — in minutes. Vandebroek believes a technology like this could help companies work through difficult decisions they need to make, such as with an acquisition.

As part of that education, companies — from the board of directors on down — need to recognize the importance of trust and transparency, according to Vandebroek. She stressed decisions be explainable and that data privacy be made a priority.

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Source: https://searchcio.techtarget.com/news/252452985/AI-enabled-enterprise-starts-with-education-not-tech

Big Data

If you did not already know

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Familia google


In the last decade, a variety of topic models have been proposed for text engineering. However, except Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA), most of existing topic models are seldom applied or considered in industrial scenarios. This phenomenon is caused by the fact that there are very few convenient tools to support these topic models so far. Intimidated by the demanding expertise and labor of designing and implementing parameter inference algorithms, software engineers are prone to simply resort to PLSA/LDA, without considering whether it is proper for their problem at hand or not. In this paper, we propose a configurable topic modeling framework named Familia, in order to bridge the huge gap between academic research fruits and current industrial practice. Familia supports an important line of topic models that are widely applicable in text engineering scenarios. In order to relieve burdens of software engineers without knowledge of Bayesian networks, Familia is able to conduct automatic parameter inference for a variety of topic models. Simply through changing the data organization of Familia, software engineers are able to easily explore a broad spectrum of existing topic models or even design their own topic models, and find the one that best suits the problem at hand. With its superior extendability, Familia has a novel sampling mechanism that strikes balance between effectiveness and efficiency of parameter inference. Furthermore, Familia is essentially a big topic modeling framework that supports parallel parameter inference and distributed parameter storage. The utilities and necessity of Familia are demonstrated in real-life industrial applications. Familia would significantly enlarge software engineers’ arsenal of topic models and pave the way for utilizing highly customized topic models in real-life problems. …

Median Absolute Deviation (MAD) google


In statistics, the median absolute deviation (MAD) is a robust measure of the variability of a univariate sample of quantitative data. It can also refer to the population parameter that is estimated by the MAD calculated from a sample. Consider the data (1, 1, 2, 2, 4, 6, 9). It has a median value of 2. The absolute deviations about 2 are (1, 1, 0, 0, 2, 4, 7) which in turn have a median value of 1 (because the sorted absolute deviations are (0, 0, 1, 1, 2, 4, 7)). So the median absolute deviation for this data is 1. …

Temporal Recurrent Network (TRN) google


Most work on temporal action detection is formulated in an offline manner, in which the start and end times of actions are determined after the entire video is fully observed. However, real-time applications including surveillance and driver assistance systems require identifying actions as soon as each video frame arrives, based only on current and historical observations. In this paper, we propose a novel framework, Temporal Recurrent Networks (TRNs), to model greater temporal context of a video frame by simultaneously performing online action detection and anticipation of the immediate future. At each moment in time, our approach makes use of both accumulated historical evidence and predicted future information to better recognize the action that is currently occurring, and integrates both of these into a unified end-to-end architecture. We evaluate our approach on two popular online action detection datasets, HDD and TVSeries, as well as another widely used dataset, THUMOS’14. The results show that TRN significantly outperforms the state-of-the-art. …

CDF2PDF google


CDF2PDF is a method of PDF estimation by approximating CDF. The original idea of it was previously proposed in [1] called SIC. However, SIC requires additional hyper-parameter tunning, and no algorithms for computing higher order derivative from a trained NN are provided in [1]. CDF2PDF improves SIC by avoiding the time-consuming hyper-parameter tuning part and enabling higher order derivative computation to be done in polynomial time. Experiments of this method for one-dimensional data shows promising results. …

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Source: https://analytixon.com/2021/06/13/if-you-did-not-already-know-1421/

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Big Data

Predict Future Sales using XGBRegressor

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XGBRegressor |Predict Future Sales using XGBRegressor





















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Source: https://www.analyticsvidhya.com/blog/2021/06/predict-future-sales-using-xgbregressor/

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Decoding the Chi-Square Test - Use, Implementation and Visualization

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Chi-Square Test - Use, Implementation and Visualization





















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Source: https://www.analyticsvidhya.com/blog/2021/06/decoding-the-chi-square-test%e2%80%8a-%e2%80%8ause-implementation-and-visualization/

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Anonymous or Lambda Functions in Python: A Beginner’s Guide!

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Lambda Functions in Python: A Beginner’s Guide! – Analytics Vidhya





















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Source: https://www.analyticsvidhya.com/blog/2021/06/anonymous-or-lambda-functions-in-python-a-beginners-guide/

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