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Success with automation and AI requires a high ‘RQ’

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Companies know that a high IQ can help drive business value. But the analyst outfit Forrester Research believes that if companies are going to successfully work side by side with artificially intelligent systems, they’re also going to need a high “RQ.”

RQ, or robotics quotient, is a measurement of how competent a company will be at automation and AI implementation. The Forrester assessment is based on three main areas: people, leadership and organizational structures. A fourth area, trust, will influence the three main categories and change depending on the type of technology being deployed.

J.P. Gownder, a Forrester analyst serving CIOs, described RQ as the “human contribution” companies need when deploying automation and AI technologies. “It’s not just about the bots; it’s not just about artificial intelligences,” he said in a July presentation at the New Tech and Innovation 2018 conference in Boston. “It’s about real people, real leaders and real organizational structures that you need to put in place to make sure you’re most likely to succeed.”

Toronto moments

Automation and AI technologies are on a spectrum from more deterministic, where A always leads to B, to more probabilistic, where A could lead to B but could also lead to C or to D.

And these probabilistic systems create a new wrinkle for companies: No matter how swanky the user interface or how cutting-edge the technology, probabilistic systems can produce incorrect — and even illogical — results that can erode the trust humans have in the machine’s abilities.

Gownder pointed to IBM Watson as an example. During its Jeopardy! debut in 2011, Watson answered a final question about U.S. cities with “Toronto,” causing the audience to gasp. When researchers did a post-mortem, it became clear that even Watson doubted the response. Using probabilistic judgement, the machine determined that Toronto had only a 30% chance of being correct, but it was the best answer it could come up with at the time.

These “Toronto moments,” as Forrester now refers to them, “teach us something about the intersection between human beings and AI and the trust that is part of this,” Gownder said.

The more probabilistic a system is, the more human intervention it might need. But designing systems and processes that strike a balance between trust and intervention will be a challenging step for companies. That’s where Forrester believes RQ will come in handy.

What is RQ?

The robotics quotient is a self-assessment that “measures the ability of individuals and organizations to learn and adapt to and collaborate with automated entities,” Gownder said. It’s composed of 39 characteristics that Forrester regards as a collection of automation and AI best practices.

Forrester Research, RQ, robotics quotient, PLOT frameworkJ.P. Gownder

The higher the score, the more prepared a company is to tackle the new challenges that come with automation and AI technologies. But RQ doesn’t just measure readiness, according to Gownder. It also enables CIOs to “identify gaps or areas where you need to prioritize resources before you make a big bet on automation and AI,” he said.

The 39 characteristics fall into one of three categories — people, leadership and organizational structure. People, for example, are measured across different dimensions — such as facilitation, which considers how effective an employee might be at communicating with an automated entity, and perception, which includes things like basic digital literacy and “constructive ambition,” or an eagerness to learn.

For leaders, the RQ highlights vision, adaptability, the ability to inspire trust and influence. The final category refers to IT employees and beyond; CIOs will need to influence the C-suite and even the board of directors to secure the budget, buy-in and support that automation and AI tools can demand. “The CIO is no longer a benign dictator who has all the power,” Gownder said. “This is the creation of an ecosystem across business units with lots of participation from the workers themselves.”

Organizational structures will also need to adapt. Automation and AI may require new titles such as bot manager, new training and mentoring opportunities for humans and machines alike, new processes that encourage human-machine team creation, and new metrics. “After all, we can have all the good intentions, and the well-educated employees and the leaders who are on board,” Gownder said, “but if we do not create structures, processes and budgets — the b word — we’re going to have a hard time getting this through.”

Don’t forget about trust

The categories of people, leadership and the organization are then measured against one final category — trust. Gownder called trust “a multiplier in this model.” Automation and AI technologies exist on a spectrum from transparent to opaque, and where the technology falls on that spectrum will influence employee trust.

“If you’re implementing something that is very transparent, that is very deterministic, your employees will bring a high level of inherent trust to the machine. They’re used to these sorts of systems,” Gownder said. “If you’re using probabilistic systems, where the machine is often uncertain of its results, then you’re going to have a higher burden of RQ investment.”

Forrester’s model breaks down the complexity of trust by providing a numeric value for how deterministic the technology is, how transparent the technology is and how much change the technology could have on the workplace.

The changes that automation and AI will have on the workplace could be a sensitive area for leaders, especially as automation and AI instigate changes in the workforce. “As you might imagine, when employees are losing their jobs as part of a deployment of automation, you magnify the mistrust among remaining employees,” Gownder said. “It raises the bar for the change management.”

But the efforts could be worthwhile. As repetitive tasks become automated, job satisfaction generally goes up, Gownder said. And although AI remains in its early stages, it is poised to transform how companies operate and interact with customers.

Whether companies choose Forrester’s RQ method or not, Gownder argued that an organizational competency in AI and automation is needed.

“If you want to be successful in creating a mixed workforce that incorporates digital workers, human workers, lots of automated processes, lots of probabilities, lots of real-time data and AI, you’re going to have to measure your people, your leaders, your organization and the inherent trust that is associated with technology,” he said.

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Source: https://searchcio.techtarget.com/feature/Success-with-automation-and-AI-requires-a-high-RQ

Big Data

If you did not already know

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SGAN google


The Generative Adversarial Networks (GANs) have demonstrated impressive performance for data synthesis, and are now used in a wide range of computer vision tasks. In spite of this success, they gained a reputation for being difficult to train, what results in a time-consuming and human-involved development process to use them. We consider an alternative training process, named SGAN, in which several adversarial ‘local’ pairs of networks are trained independently so that a ‘global’ supervising pair of networks can be trained against them. The goal is to train the global pair with the corresponding ensemble opponent for improved performances in terms of mode coverage. This approach aims at increasing the chances that learning will not stop for the global pair, preventing both to be trapped in an unsatisfactory local minimum, or to face oscillations often observed in practice. To guarantee the latter, the global pair never affects the local ones. The rules of SGAN training are thus as follows: the global generator and discriminator are trained using the local discriminators and generators, respectively, whereas the local networks are trained with their fixed local opponent. Experimental results on both toy and real-world problems demonstrate that this approach outperforms standard training in terms of better mitigating mode collapse, stability while converging and that it surprisingly, increases the convergence speed as well. …

Expert-Augmented Machine Learning (EAML) google


Machine Learning is proving invaluable across disciplines. However, its successis often limited by the quality and quantity of available data, while its adoption by the level of trust that models afford users. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of man and machine. Here we present Expert-Augmented Machine Learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We use a large dataset of intensive care patient data to predict mortality and show that we can extract expert knowledge using an online platform, help reveal hidden confounders, improve generalizability ona different population and learn using less data. EAML presents a novel framework for high performance and dependable machine learning in critical applications. …

Multiple Independent Subspace Clustering (MISC) google


Multiple clustering aims at discovering diverse ways of organizing data into clusters. Despite the progress made, it’s still a challenge for users to analyze and understand the distinctive structure of each output clustering. To ease this process, we consider diverse clusterings embedded in different subspaces, and analyze the embedding subspaces to shed light into the structure of each clustering. To this end, we provide a two-stage approach called MISC (Multiple Independent Subspace Clusterings). In the first stage, MISC uses independent subspace analysis to seek multiple and statistical independent (i.e. non-redundant) subspaces, and determines the number of subspaces via the minimum description length principle. In the second stage, to account for the intrinsic geometric structure of samples embedded in each subspace, MISC performs graph regularized semi-nonnegative matrix factorization to explore clusters. It additionally integrates the kernel trick into matrix factorization to handle non-linearly separable clusters. Experimental results on synthetic datasets show that MISC can find different interesting clusterings from the sought independent subspaces, and it also outperforms other related and competitive approaches on real-world datasets. …

Fractal AI google


Fractal AI is a theory for general artificial intelligence. It allows to derive new mathematical tools that constitute the foundations for a new kind of stochastic calculus, by modelling information using cellular automaton-like structures instead of smooth functions. In the repository included we are presenting a new Agent, derived from the first principles of the theory, which is capable of solving Atari games several orders of magnitude more efficiently than other similar techniques, like Monte Carlo Tree Search. The code provided shows how it is now possible to beat some of the current state of the art benchmarks on Atari games, without previous learning and using less than 1000 samples to calculate each one of the actions when standard MCTS uses 3 Million samples. Among other things, Fractal AI makes it possible to generate a huge database of top performing examples with very little amount of computation required, transforming Reinforcement Learning into a supervised problem. The algorithm presented is capable of solving the exploration vs exploitation dilemma on both the discrete and continuous cases, while maintaining control over any aspect of the behavior of the Agent. From a general approach, new techniques presented here have direct applications to other areas such as: Non-equilibrium thermodynamics, chemistry, quantum physics, economics, information theory, and non-linear control theory. …

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Source: https://analytixon.com/2021/06/15/if-you-did-not-already-know-1423/

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KDnuggets Top Blogs Rewards for May 2021

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KDnuggets Top Blogs Rewards for May 2021

We announce the winners of the first KDnuggets Top Blog Rewards Program.


Top Blog RewardsCongratulations to winners of KDnuggets Top Blogs Reward Program for May 2021, which will share the total amount of $2,000 (USD) according to the unique page views in the first 21 days starting with the day of publication.

  1. Vaex: Pandas but 1000x faster, by Ahmad Anis (original)
  2. Data Preparation in SQL, with Cheat Sheet!, by Stan Pugsley (original)
  3. A Guide On How To Become A Data Scientist (Step By Step Approach), by Aditya Agarwal
  4. Top Programming Languages and Their Uses, by Claire D. Costa (original)
  5. Essential Linear Algebra for Data Science and Machine Learning, by Benjamin Obi Tayo (original)
  6. Charticulator: Microsoft Research open-sourced a game-changing Data Visualization platform, by Josh Taylor

We started this program to encourage more high-quality and especially original (unpublished) contributions to KDnuggets.

For each month, starting in May 2021, we will determine 6 most viewed blogs published on KDnuggets that month (excluding blogs by KDnuggets Editors Matthew Mayo and Gregory Piatetsky), and will distribute $2,000 (USD) among the authors of these top blogs. If a blog has multiple authors, we will send the payment to the first author and it will be their responsibility to distribute it. If the author declines the monetary reward, the amount will roll over to the next month rewards.

While we appreciate and publish the reposts, the goal of this program is to get more great original publications, which will be rewarded at the rate of 3X the reposts. Note: An original submission first published on KDnuggets may be reposted elsewhere two weeks after KDnuggets publication, provided there is a link to KDnuggets blog and text “Originally published on KDnuggets”.

Here are the details on KDnuggets Top Blogs Reward Program .

To submit a blog to KDnuggets, please follow the submission guidelines.

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Source: https://www.kdnuggets.com/2021/06/top-blogs-rewards-may.html

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Americans are curious about electric trucks, but low cost, durability come first – Reuters/Ipsos poll

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By Tina Bellon

(Reuters) – While most Americans expect electric vehicles eventually to dominate the nation’s roadways, many have greeted the first battery-powered models with skepticism, expressing concerns about the potential costs and inconveniences of owning such vehicles, a Reuters/Ipsos poll showed.

Automakers have already recognized the challenge and said they plan to market future battery-powered models by touting their performance and long-term cost savings over environmental benefits.

Vehicle manufacturers need to win over more rural Americans and Republicans, with the survey showing those groups express less enthusiasm than others for electric vehicles.

Vehicle electrification, particularly of the most popular large, fuel-intensive light trucks and SUVs, has become a central part of U.S. efforts to reduce greenhouse gas emissions to combat climate change. In the Reuters/Ipsos national poll of around 1,005 adults from June 3-4, 67% of all respondents agreed in a bipartisan consensus that pickup trucks are the American way of life, underscoring the focus on electrifying those vehicles to reach climate goals.

Rural consumers in so-called red states or Republican strongholds are a key demographic for pickup trucks, the auto industry’s most profitable vehicle segment, challenging automakers to navigate an electric future without alienating those customers.

Several automakers, including Ford Motor Co and General Motors Co, are planning to release all-electric trucks in the coming years. But no electric pickup truck is yet available, which means consumer attitudes about them are largely based on second-hand knowledge.

Graphic-What do you want in a truck? https://graphics.reuters.com/AUTOS-ELECTRIC/SURVEY/qzjpqrgynvx/chart_eikon.jpg

A majority of 65% agreed that electric vehicles are the future of the auto industry, the poll showed. While there were clear divisions along party lines – with 53% agreeing among Republicans, compared with 78% among Democrats – the issue is less partisan than other contentious U.S. political topics.

Nearly half of Americans – 46% – believe EVs are not worth the cost, the survey showed, with opinions again reflecting a political divide. EVs have a higher sticker price than comparable gasoline models, largely due to costly batteries.

When asked to consider buying a new truck, only a minority said they would search for one that minimizes its impact on the environment. Thirty-eight percent said they would look first for an efficient truck that costs less to operate. Another 34% said they wanted a durable truck, and only about 19% said they wanted an environmentally friendly one.

PERFORMANCE BENEFITS

Jim Richman, a window installer in Austin, Texas, who owns a 2018 Ford F-150, said he is also concerned about the reliability of electric trucks and the hassle of charging them.

“I just want to know my truck is ready whenever I need it, and also for longer trips,” Richman said, echoing a sentiment among a majority of survey respondents who said EVs generally were inconvenient because of their need for charging.

The auto industry is trying to alleviate those concerns with technological innovations, including reductions in battery costs and longer driving ranges.

GM, which plans to start selling an all-electric version of its Hummer pickup by the end of next year, said more consumers will consider EVs as they become familiar with battery-powered vehicles. GM is also trying to emphasize economic benefits of going electric.

“We have a tremendous opportunity to help consumers understand that benefits of driving electric aren’t just related to the environmental benefits, but performance and capability that EVs offer,” said Darryll Harrison, a GM spokesman.

Researchers expect EVs to reach price parity with fuel-powered counterparts around 2030, and EVs also provide long-term cost savings due to lower maintenance costs and the lower cost per mile of refueling with electricity instead of gasoline.

Ford said its research had shown consumer demand for an e-truck, especially among younger truck owners. The company plans to release its all-electric F-150 Lightning truck next year, for which it has received some 100,000 reservations to date.

“By 2030 we expect 40% of our global mix to be fully electric and we’ll achieve that by electrifying our most popular name plates,” said Darren Palmer, Ford’s North America EV general manager.

Chris Sherman, general manager of a Ford dealership in rural Paris, Texas, said he has been surprised by the positive response to the Lightning so far.

“These are pretty much diehard gas and diesel pickup owners,” Sherman said. “Lot of people here are blue-collar, and they tow utility trailers. They want to know how much of a day’s work they can do with their truck.”

Sherman said he had so far received 10 reservations for the electric truck and that he fields up to 40 phone calls per day about it.

The Reuters/Ipsos poll was conducted online, in English, throughout the United States. It gathered responses from 1,005 adults, and the results have a credibility interval, a measure of precision, of about 4 percentage points.

(Reporting by Tina Bellon in Austin, Texas; Additional reporting by Paul Lienert in Detroit; Editing by Joe White, Chris Kahn and Matthew Lewis)

Image Credit: Reuters

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Source: https://datafloq.com/read/americans-curious-electric-trucks-low-cost-durability-come-first-reutersipsos-poll/15475

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GM’s self-driving car unit Cruise to access $5 billion credit line

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BERKELEY, California (Reuters) -General Motors Co’s majority owned Cruise self-driving car subsidiary said on Tuesday it would gain access to a multi-year $5 billion line of credit with GM’s finance arm to fund its purchase of Origin vehicles starting in early 2023.

“This bumps up Cruise’s total war chest to over $10 billion as we enter commercialization,” Dan Ammann, chief executive of the Cruise unit, which also counts Softbank and Honda as minority investors, said in a blog posting.

Self-driving startups are racing to build warchests to develop and scale up technology, an expensive and time-consuming process.

This past month, General Motors began assembling the first of about 100 pre-production Cruise Origin all-electric vehicles to be built this summer for validation testing, Cruise said.

Cruise, which has been testing driverless GM Chevy Bolt electric vehicles in San Francisco, plans to commercialize the technology in the densely populated city “in months, not years,” spokesman Ray Wert said.

Reuters last month reported that Cruise and rival Waymo, a unit of Alphabet Inc’s, have applied for permits needed to start charging for autonomous rides and delivery in San Francisco.

(Reporting by Hyunjoo Jin; Editing by Edmund Blair and David Gregorio)

Image Credit: Reuters

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Source: https://datafloq.com/read/gms-self-driving-car-unit-cruise-access-5-billion-credit-line/15474

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