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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.

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

Big Data

How much Mathematics do you need to know for Machine Learning?

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Mathematics For Machine Learning | Maths to understand ML Algorithms





















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Source: https://www.analyticsvidhya.com/blog/2021/07/how-much-mathematics-do-you-need-to-know-for-machine-learning/

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If you did not already know

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ML Health google


Deployment of machine learning (ML) algorithms in production for extended periods of time has uncovered new challenges such as monitoring and management of real-time prediction quality of a model in the absence of labels. However, such tracking is imperative to prevent catastrophic business outcomes resulting from incorrect predictions. The scale of these deployments makes manual monitoring prohibitive, making automated techniques to track and raise alerts imperative. We present a framework, ML Health, for tracking potential drops in the predictive performance of ML models in the absence of labels. The framework employs diagnostic methods to generate alerts for further investigation. We develop one such method to monitor potential problems when production data patterns do not match training data distributions. We demonstrate that our method performs better than standard ‘distance metrics’, such as RMSE, KL-Divergence, and Wasserstein at detecting issues with mismatched data sets. Finally, we present a working system that incorporates the ML Health approach to monitor and manage ML deployments within a realistic full production ML lifecycle. …

Guided Zoom google


We propose Guided Zoom, an approach that utilizes spatial grounding to make more informed predictions. It does so by making sure the model has ‘the right reasons’ for a prediction, being defined as reasons that are coherent with those used to make similar correct decisions at training time. The reason/evidence upon which a deep neural network makes a prediction is defined to be the spatial grounding, in the pixel space, for a specific class conditional probability in the model output. Guided Zoom questions how reasonable the evidence used to make a prediction is. In state-of-the-art deep single-label classification models, the top-k (k = 2, 3, 4, …) accuracy is usually significantly higher than the top-1 accuracy. This is more evident in fine-grained datasets, where differences between classes are quite subtle. We show that Guided Zoom results in the refinement of a model’s classification accuracy on three finegrained classification datasets. We also explore the complementarity of different grounding techniques, by comparing their ensemble to an adversarial erasing approach that iteratively reveals the next most discriminative evidence. …

UniParse google


This paper describes the design and use of the graph-based parsing framework and toolkit UniParse, released as an open-source python software package. UniParse as a framework novelly streamlines research prototyping, development and evaluation of graph-based dependency parsing architectures. UniParse does this by enabling highly efficient, sufficiently independent, easily readable, and easily extensible implementations for all dependency parser components. We distribute the toolkit with ready-made configurations as re-implementations of all current state-of-the-art first-order graph-based parsers, including even more efficient Cython implementations of both encoders and decoders, as well as the required specialised loss functions. …

Sparse Constraint Preserving Matching (SPM) google


Many problems of interest in computer vision can be formulated as a problem of finding consistent correspondences between two feature sets. Feature correspondence (matching) problem with one-to-one mapping constraint is usually formulated as an Integral Quadratic Programming (IQP) problem with permutation (or orthogonal) constraint. Since it is NP-hard, relaxation models are required. One main challenge for optimizing IQP matching problem is how to incorporate the discrete one-to-one mapping (permutation) constraint in its quadratic objective optimization. In this paper, we present a new relaxation model, called Sparse Constraint Preserving Matching (SPM), for IQP matching problem. SPM is motivated by our observation that the discrete permutation constraint can be well encoded via a sparse constraint. Comparing with traditional relaxation models, SPM can incorporate the discrete one-to-one mapping constraint straightly via a sparse constraint and thus provides a tighter relaxation for original IQP matching problem. A simple yet effective update algorithm has been derived to solve the proposed SPM model. Experimental results on several feature matching tasks demonstrate the effectiveness and efficiency of SPM method. …

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Source: https://analytixon.com/2021/07/29/if-you-did-not-already-know-1461/

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Nokia lifts full-year forecast as turnaround takes root

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HELSINKI (Reuters) -Telecom equipment maker Nokia reported a stronger-than-expected second-quarter operating profit on Thursday and raised its full-year outlook as promised, thanks to a turnaround of its business.

The Finnish company’s April-June comparable operating profit rose to 682 million euros ($808.51 million) from 423 million euros a year earlier, beating the 408-million euro mean estimate in a Refinitiv poll of analysts.

Shifting geopolitics and a sharp round of cost cutting have put Nokia firmly back in the global 5G rollout race just a year after CEO Pekka Lundmark took the reins, allowing it to gain ground on Swedish arch-rival Ericsson.

“We have executed faster than planned on our strategy in the first half which provides us with a good foundation for the full year,” Lundmark said in a statement on Thursday, but added that Nokia still expects the 2021 second-half results to be less pronounced.

Nokia said it now expects full-year net sales of 21.7 billion-22.7 billion euros, up from its prior estimate of 20.6 billion-21.8 billion euros, with an operating profit margin of 10-12% instead of the 7% to 10% expected previously.

The company had announced on July 13 that it would raise its outlook, but did not provide any details.

($1 = 0.8435 euros)

(Reporting by Essi Lehto; editing by Terje Solsvik and Sriraj Kaluvila)

Image Credit: Reuters

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Source: https://datafloq.com/read/nokia-lifts-full-year-forecast-turnaround-takes-root/16713

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Robinhood, gateway to ‘meme’ stocks, raises $2.1 billion in IPO

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By Echo Wang and David French

(Reuters) -Robinhood Markets Inc, the owner of the trading app which emerged as the go-to destination for retail investors speculating on this year’s “meme’ stock trading frenzy, raised $2.1 billion in its initial public offering on Wednesday.

The company was seeking to capitalize on individual investors’ fascination with cryptocurrencies and stocks such as GameStop Corp, which have seen wild swings after becoming the subject of trading speculation on social media sites such as Reddit. Robinhood’s monthly active users surged from 11.7 million at the end of December to 21.3 million as of the end of June.

The IPO valued Robinhood at $31.8 billion, making it greater as a function of its revenue than many of its traditional rivals such as Charles Schwab Corp, but the offering priced at the bottom of the company’s indicated range.

Some investors stayed on the sidelines, citing concerns over the frothy valuation, the risk of regulators cracking down on Robinhood’s business, and even lingering anger with the company’s imposition of trading curbs when the meme stock trading frenzy flared up at the end of January.

Robinhood said it sold 55 million shares in the IPO at $38 apiece, the low end of its $38 to $42 price range. This makes it one of the most valuable U.S. companies to have gone public year-to-date, amid a red-hot market for new listings.

In an unusual move, Robinhood had said it would reserve between 20% and 35% of its shares for its users.

Robinhood’s platform allows users to make unlimited commission-free trades in stocks, exchange-traded funds, options and cryptocurrencies. Its simple interface made it popular with young investors trading from home during the COVID-19 pandemic.

Robinhood enraged some investors and U.S. lawmakers earlier this year when it restricted trading in some popular stocks following a 10-fold rise in deposit requirements at its clearinghouse. It has been at the center of many regulatory probes.

The company disclosed this week that it has received inquiries from U.S. regulators looking into whether its employees traded shares of GameStop and AMC Entertainment Holdings, Inc before the trading curbs were placed at the end of January.

In June, Robinhood agreed to pay nearly $70 million to settle an investigation by Wall Street’s own regulator, the Financial Industry Regulatory Authority, for “systemic” failures, including systems outages, providing “false or misleading” information, and weak options trading controls.

The brokerage has also been criticized for relying on “payment for order flow” for most of its revenue, under which it receives fees from market makers for routing trades to them and does not charge users for individual trades.

Critics argue the practice, which is used by many other brokers, creates a conflict of interest, on the grounds that it incentivizes brokers to send orders to whoever pays the higher fees. Robinhood contends that it routes trades based on what is cheapest for its users, and that charging a commission would be more expensive. The U.S. Securities and Exchange Commission is examining the practice.

Robinhood was founded in 2013 by Stanford University roommates Vlad Tenev and Baiju Bhatt. They will hold a majority of the voting power after the offering, these filings showed, with Bhatt having around 39% of the voting power of outstanding stock while Tenev will hold about 26.2%.

The company’s shares are scheduled to start trading on Nasdaq on Thursday under the ticker “HOOD”

Goldman Sachs and J.P. Morgan were the lead underwriters in Robinhood’s IPO.

(Reporting by Echo Wang and David French in New York; Editing by Leslie Adler)

Image Credit: Reuters

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Source: https://datafloq.com/read/robinhood-gateway-meme-stocks-raises-21-billion-ipo/16712

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