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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

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Deepfake detectors and datasets exhibit racial and gender bias, USC study shows

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Some experts have expressed concern that machine learning tools could be used to create deepfakes, or videos that take a person in an existing video and replace them with someone else’s likeness. The fear is that these fakes might be used to do things like sway opinion during an election or implicate a person in a crime. Already, deepfakes have been abused to generate pornographic material of actors and defraud a major energy producer.

Fortunately, efforts are underway to develop automated methods to detect deepfakes. Facebook — along with Amazon  and Microsoft, among others — spearheaded the Deepfake Detection Challenge, which ended last June. The challenge’s launch came after the release of a large corpus of visual deepfakes produced in collaboration with Jigsaw, Google’s internal technology incubator, which was incorporated into a benchmark made freely available to researchers for synthetic video detection system development. More recently, Microsoft launched its own deepfake-combating solution in Video Authenticator, a system that can analyze a still photo or video to provide a score for its level of confidence that the media hasn’t been artificially manipulated.

But according to researchers at the University of Southern California, some of the datasets used to train deepfake detection systems might underrepresent people of a certain gender or with specific skin colors. This bias can be amplified in deepfake detectors, the coauthors say, with some detectors showing up to a 10.7% difference in error rate depending on the racial group.

Biased deepfake detectors

The results, while surprising, are in line with previous research showing that computer vision models are susceptible to harmful, pervasive prejudice. A paper last fall by University of Colorado, Boulder researchers demonstrated that AI from Amazon, Clarifai, Microsoft, and others maintained accuracy rates above 95% for cisgender men and women but misidentified trans men as women 38% of the time. Independent benchmarks of major vendors’ systems by the Gender Shades project and the National Institute of Standards and Technology (NIST) have demonstrated that facial recognition technology exhibits racial and gender bias and have suggested that current facial recognition programs can be wildly inaccurate, misclassifying people upwards of 96% of the time.

The University of Southern California group looked a three deepfake detection models with “proven success in detecting deepfake videos.” All were trained on the FaceForensics++ dataset, which is commonly used for deepfake detectors, as well as corpora including Google’s DeepfakeDetection, CelebDF, and DeeperForensics-1.0.

In a benchmark test, the researchers found that all of the detectors performed worst on videos with darker Black faces, especially male Black faces. Videos with female Asian faces had the highest accuracy, but depending on the dataset, the detectors also performed well on Caucasian (particularly male) and Indian faces. .

According to the researchers, the deepfake detection datasets were “strongly” imbalanced in terms of gender and racial groups, with FaceForensics++ sample videos showing over 58% (mostly white) women compared with 41.7% men. Less than 5% of the real videos showed Black or Indian people, and the datasets contained “irregular swaps,” where a person’s face was swapped onto another person of a different race or gender.

These irregular swaps, while intended to mitigate bias, are in fact to blame for at least a portion of the bias in the detectors, the coauthors hypothesize. Trained on the datasets, the detectors learned correlations between fakeness and, for example, Asian facial features. One corpus used Asian faces as foreground faces swapped onto female Caucasian faces and female Hispanic faces.

“In a real-world scenario, facial profiles of female Asian or female African are 1.5 to 3 times more likely to be mistakenly labeled as fake than profiles of the male Caucasian … The proportion of real subjects mistakenly identified as fake can be much larger for female subjects than male subjects,” the researchers wrote.

Real-world risks

The findings are a stark reminder that even the “best” AI systems aren’t necessarily flawless. As the coauthors note, at least one deepfake detector in the study achieved 90.1% accuracy on a test dataset, a metric that conceals the biases within.

“[U]sing a single performance metrics such as … detection accuracy over the entire dataset is not enough to justify massive commercial rollouts of deepfake detectors,” the researchers wrote. “As deepfakes become more pervasive, there is a growing reliance on automated systems to combat deepfakes. We argue that practitioners should investigate all societal aspects and consequences of these high impact systems.”

The research is especially timely in light of growth in the commercial deepfake video detection market. Amsterdam-based Deeptrace Labs offers a suite of monitoring products that purport to classify deepfakes uploaded on social media, video hosting platforms, and disinformation networks. Dessa has proposed techniques for improving deepfake detectors trained on data sets of manipulated videos. And Truepic raised an $8 million funding round in July 2018 for its video and photo deepfake detection services. In December 2018, the company acquired another deepfake “detection-as-a-service” startup — Fourandsix — whose fake image detector was licensed by DARPA.

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Source: https://venturebeat.com/2021/05/06/deepfake-detectors-and-datasets-exhibit-racial-and-gender-bias-usc-study-shows/

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U.S. listing ban on Luokung lifted after judge’s decision

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By Karen Freifeld

WASHINGTON (Reuters) – Nasdaq Inc has withdrawn a decision to delist the shares of Luokung Technology Corp, the Chinese mapping technology company said on Thursday, after a U.S. judge suspended an imminent investment ban imposed under the Trump administration.

The ruling and listing news sent shares of the company nearly 20% higher. Luokung issued a news release on Thursday saying Nasdaq notified the company it has withdrawn its delisting letter and shares would continue to trade on the market, not be suspended on May 7. A Nasdaq spokesman declined to comment.

A spokesman for the U.S. Department of Justice did not immediately respond to a request for comment.

Luokung is the second company on a U.S. list of alleged Communist Chinese military companies subject to an investment ban to win a preliminary injunction halting the designation. 

U.S. District Court Judge Rudolph Contreras in Washington issued a similar order in March in favor of Beijing-based smartphone maker Xiaomi Corp.

In granting an injunction in the case brought by Luokung challenging the ban, Contreras said the U.S. Department of Defense’s designation process was flawed.

“Many of the associations the Department of Defense seemed most troubled by – such as Luokung’s purported forays into space systems or its potential future contracts with the Chinese National Geospatial Information Center … do not appear to have materialized, nor are they likely to bear fruit before this case can be decided on the merits,” the judge wrote in his decision.

He added the government has not identified a single technology transfer from Luokung to the People’s Republic of China.

More than 40 companies were added to a list of U.S. companies subject to the investment ban in the waning days of the Trump administration.

(Reporting by Karen Freifeld in New York; Editing by Matthew Lewis)

Image Credit: Reuters

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Source: https://datafloq.com/read/us-listing-ban-luokung-lifted-judges-decision/14477

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Nikola may seal order for 100 EVs from port trucking firm

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(Reuters) – Nikola Corp said on Thursday it signed a letter of intent with port trucking firm Total Transportation Services Inc for trials and possible order of 100 vehicles from the electric-truck maker.

Shares of the Phoenix, Arizona-based company surged 7.5% in premarket trading after the announcement.

Total Transportation is a large player at the ports of Los Angeles and Long Beach in southern California, which see up to 14,000 trucks on location in a month.

“The LOI is for 100 zero-emission trucks beginning with a four-truck pilot slated to start in the first half of 2022 and consisting of two BEVs and two FCEVs,” said Nikola.

Nikola’s Tre BEVs are designed for local deliveries up to 350 miles, while its Tre FCEV trucks are aimed at longer hauls of up to 500 miles.

(Reporting by Tiyashi Datta in Bengaluru; Editing by Ramakrishnan M.)

Image Credit: Reuters

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Source: https://datafloq.com/read/nikola-may-seal-order-100-evs-port-trucking-firm/14476

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North American companies buying more robots to keep up with demand

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By Timothy Aeppel

(Reuters) – North American companies boosted spending on industrial robots in the first quarter as they scrambled to keep up with surging demand in the wake of the COVID-19 pandemic.

Companies ordered 9,098 robots in the first quarter, a 19.6% increase over a year ago, according to the Association for Advancing Automation, an industry group based in Ann Arbor, Michigan. The orders were valued at over $466 million in total.

Robots were once concentrated in the auto industry but are now moving into more corners of the economy, from ecommerce warehouses to food processing plants. For the first time last year, most of the robots ordered by companies in North America weren’t destined for auto factories or their parts suppliers.

The strongest growth in the latest quarter was to metal producers, where orders surged 86%. Orders to life science, pharmaceutical and biomedical companies rose 72%, while orders to consumer goods companies increased 32%.

“The strong economy obviously helps,” said Jeff Burnstein, president of the Association for Advancing Automation, “It gives companies the confidence to invest in more things — including in more automation.”

Burnstein said the pandemic froze many businesses, as operations shut down to protect human health. “But ultimately it accelerated the adoption of automation, because companies recognized if they were going to do it, now would be the time.”

Tyson Foods Inc, the U.S. meat company, is among those looking to use more robots on its production lines. In 2019, the company opened a 26,000 square foot automation research center near its headquarters in Springdale, Arkansas.

“For the most part, it’s still too soon for some of the really innovative and proprietary systems we’re developing,” said Marty Linn, the center’s director of engineering. Automating jobs such as deboning chickens is extremely difficult, he noted, because the size and shape of each chicken can vary greatly. Robots work best when they can handle uniform items.

With that in mind, Linn said, Tyson has already started installing at its plants robots that sit at the end of production lines and automatically stack and wrap boxes on pallets – a process that involves standard shapes and the repetition of precise movements.

(Reporting by Timothy Aeppel; Editing by Dan Burns and Andrea Ricci)

Image Credit: Reuters

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Source: https://datafloq.com/read/north-american-companies-buying-robots-keep-demand/14475

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