World’s largest sailing cats return to Whangrei – New Zealand Herald

Mouse Trap arriving in Whangrei Harbour with fenders placed, ready for docking. Photo / Jodi Bryant.

One of the world's largest sailing catamarans has arrived in Whangrei Harbour to undergo a major refit.

Mouse Trap, a 34m superyacht, has returned to Oceania Marine Refit Services for the second time in a year to undergo three months of maintenance, a full paint job and new teak decks.

Built in 2012, Mouse Trap, which sleeps eight and with a crew of six, has been based in the South Pacific for the last three years and has returned to Whangrei for servicing, Oceania Marine Refit Services client liaison and marketing manager Jim Loynes said.

"She's been going between there and us for around three years. We looked after them last time so the plan was always that they would return."

He said Mouse Trap had been "one of the lucky ones" - one of six, he believes, to be let into the country with a border exemption for refit work.

The process involved Covid-testing before departure, two weeks' quarantine carried out both during the voyage and upon arrival, followed by another test before both crew and vessel left the dock.

The luxury vessel, which reaches a top speed of 12 knots, boasts a dining area with a 360-degree view over the ocean, an entertainment and relaxation area, a spa pool and sun loungers. An additional exterior salon has a sun lounge and bar.

Mouse Trap is one of two of the world's largest cruising catamarans to return to Oceania Marine Refit Services within a year. Douce France which is 42m and currently in French Polynesia, is set to return as well.

One of the most famous sailing yachts in the world, Douce France is secretly known as "the gentle giant" and said to also be one of the most prestigious offerings in the luxury-crewed yacht charter market today.

It has a unique 250-bottle wine cellar, with a selection of the world's most famous wines on board.

Last month two luxury vessels left Port Nikau after being worked on in Whangrei.

They were Odyssey, belonging to New Zealand's richest man, Graeme Hart, and Imagine, an (approximate) 44m luxury performance/cruising yacht.

Odyssey had been undergoing a refit - its second maintenance visit to Whangrei in the past year, while it is believed Imagine had the mast removed before heading to Auckland for maintenance and returning to Whangrei to have the mast refitted.

Whangrei's growing marine industry has increasingly been attracting luxurious yachts, with the impressive line-up visible at Port Nikau from across the harbour at Onerahi.

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World's largest sailing cats return to Whangrei - New Zealand Herald

Can Artificial Intelligence Help Students Work Better Together? According to Research, the Answer is Yes. – WPI News

Once the AI Partners are integrated in these classrooms, Whitehill and his team will be able to collect data on how students interact with them, and then iteratively make them more intelligent and effective. Initially, the AI Partner might be controlled by a human teacher in a backroom (Wizard of Oz-style interaction), but over time, it can learn from its human controller what to do when and thereby become more autonomous. Whitehill and his team anticipate that the particular form that the Partner takes is likely also important.

Students might find an embodied robot creepy, but they might like interacting with an animated avatar on a touchscreen, he says.

This project represents a shift in how researchers envision AI in the classroom. While earlier work in this field sought to fully automate the teaching process, which Whitehill considers to be infeasible, this project is about human-AI teaming, and how humans and teachers possess complementary abilities. AI Partners can help to magnify teachers existing strengths by increasing the number of students in the classroom who receive the real-time feedback they need for optimal learning.

Whitehill also says that this research will be greatly informative even during the COVID-19 pandemic, when many school districts across the country are participating in remote learning. In fact, he says testing agent-student interactions over platforms like Zoom have certain advantages over in-person interactions.

With Zoom, each student and teacher in the classroom is cleanly separated from each other, and all their audiovisual inputs are channeled through a common software interface. This makes it much easier to analyze their speech, gestures, language, and interactions with each other, Whitehill says. In contrast, in normal, in-person classrooms, the interactions are much messier, since students often sit in all kinds of different positions, might be touching their faces, and work in a noisy environment, which makes it more challenging for the Partner to observe and analyze.

By the end of this research, Whitehill says he hopes to find practical teaching and coaching strategies that AI Partners can execute that work well with students. Its not clear at all that the way humans teach would work well for a computer, robot, or avatar, he says.

While the computational challenges of the projectsignal processing in extremely noisy and cluttered settings, real-time control in an uncertain environment, and human-computer interaction for a novel settingare formidable, Whitehill says the potential rewards make it worth the effort.

The exciting thing about this project is that we get to completely rethink the role of AI in the classroom, he says. My hope is that, through next-generation educational AI, we will be able to stimulate deeper critical thinking and collaboration among students to help them learn better and achieve more.

Jessica Messier

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Can Artificial Intelligence Help Students Work Better Together? According to Research, the Answer is Yes. - WPI News

Defense Official Calls Artificial Intelligence the New Oil – Department of Defense

Artificial intelligence is the new oil, and the governments or the countries that get the best datasets will unquestionably develop the best AI, the Joint Artificial Intelligence Center's chief technologyofficer said Oct. 15.

Speaking on a panel about AI superpowers at the Politico AI Summit, Nand Mulchandani said AI is a very large technology and industry. "It's not a single, monolithic technology," he said. "It's a collection of algorithms, technologies, etc., all cobbled together to call AI."

The United States has access to global datasets, and that's why global partnerships are so incredibly important, he said, noting the Defense Department launched the AI partnership for defense at the JAIC recently to have access to global datasets with partners, which gives DOD a natural advantage in building these systems at scale.

"Industry has to develop on its own, and that's where the global talent is; that's where the money is; that's where all of the innovation is going on," Mulchandani noted, adding that the U.S. government's job is to be able to work in the best way and absorb the best technology that it can. That includes working hand in glove with industry on a voluntary basis, he said. He said there are certain areas of AI that are highly scaled that you can trust and deploy at scale.

"But notice many or not many of those systems have been deployed on weapon systems. We actually don't have any of them deployed," he said.

Mulchandani said the reason is that explainability, testing, trust and ethics are all highly connected pieces and even AI security when it comes to model security, data security being able to penetrate and break models. This is all very early, which is why the DOD and the U.S. government widely have taken a very stringent approach to putting together the ethics principles and frameworks within which we're going to operate.

"[Earlier this year, one of the first international visits that we made were to NATO and our European partners, and [we] then pulled them into this AI partnership for defense that I just talked about," he said. "Thirteen different countries are getting together to actually build these principles because we actually do need to build a lot of confidence in this."

He said DOD continues to attract and have the best talent at JAIC. "The real tricky part is: How do we actually take that technology and get it deployed? That's the complexity of integrating AI into existing systems, because one isn't going to throw away the entire investment of legacy systems that one has, whether it be software or hardware or even military hardware," Mulchandani said. "[How] can we absorb the best of what's coming and get it integrated into the system as where the complexity is?"

DOD has had a long history of companies that know how to do that, and harnessing it is the actual work and the piece that we're worried about the most and really are focused on the most, he added.

A global workforce the DOD technology companies are global companies, he emphasized. "These are not linked to a particular geographic region. We hire. We bring the best talent in, wherever it may be, [and we have] research and development arms all over the world."

DOD has special security needs and requirements that must be taken care of when it comes to data, and the JAIC is putting in place very different development processes now to handle AI development, he said. "So, the dynamics of the way software gets built [and] the dynamics of who builds it are changing in a very significant way," Mulchandani said. "But the global war for talent is a real one, which is why we are not actually focused on trying to corner the market on talent."

He said they are trying to build leverage by building relationships with the leading AI companies to harness the innovation.

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Defense Official Calls Artificial Intelligence the New Oil - Department of Defense

Artificial intelligence gets real in the OR – Modern Healthcare

Dr. Ahmed Ghazi, a urologist and director of the simulation innovation lab at the University of Rochester (N.Y.) Medical Center, once thought autonomous robotic surgery wasnt possible. He changed his mind after seeing a research group successfully complete a running suture on one of his labs tissue models with an autonomous robot.

It was surprisingly preciseand impressive, Ghazi said. But whats missing from the autonomous robot is the judgment, he said. Every single patient, when you look inside to do the same surgery, is very different. Ghazi suggested thinking about autonomous surgical procedures like an airplane on autopilot: the pilots still there. The future of autonomous surgery is there, but it has to be guided by the surgeon, he said.

Its also a matter of ensuring AI surgical systems are trained on high-quality and representative data, experts say. Before implementing any AI product, providers need to understand what data the program was trained on and what data it considers to make its decisions, said Dr. Andrew Furman, executive director of clinical excellence at ECRI. What data were input for the software or product to make a particular decision must also be weighed, and are those inputs comparable to other populations? he said.

To create a model capable of making surgical decisions, developers need to train it on thousands of previous surgical cases. That could be a long-term outcome of using AI to analyze video recordings of surgical procedures, said Dr. Tamir Wolf, co-founder and CEO of Theator, another company that does just that.

While the companys current product is designed to help surgeons prepare for a procedure and review their performance, its vision is to use insights from that data to underpin real-time decision support and, eventually, autonomous surgical systems.

UC San Diego Health is using a video-analysis tool developed by Digital Surgery, an AI and analytics company Medtronic acquired earlier this year. The acquisition is part of Medtronics strategy to bolster its AI capabilities, said Megan Rosengarten, vice president and general manager of surgical robotics at Medtronic.

Theres a lot of places where were going to build upon that, Rosengarten said. She described a likely evolution from AI providing recommendations for nonclinical workflows, to offering intra-operative clinical decision support, to automating aspects of nonclinical tasks, and possibly to automating aspects of clinical tasks.

Autonomous surgical robots arent a specific end goal Medtronic is aiming for, she said, though the companys current work could serve as building blocks for automation.

Intuitive Surgical, creator of the da Vinci system, isnt actively looking to develop autonomous robotic systems, according to Brian Miller, the companys senior vice president and general manager for systems, imaging and digital.Its AI products so far use the technology to create 3D visualizations from images and extract insights from how surgeons interact with the companys equipment.

To develop an automated robotic product, it would have to solve a real problem identified by customers, Miller said, which he hasnt seen. Were looking to augment what the surgeon or what the users can do, he said.

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Artificial intelligence gets real in the OR - Modern Healthcare

AI that scans a construction site can spot when things are falling behind – MIT Technology Review

When managers tour a site once or twice a week, the camera on their head captures video footage of the whole project and uploads it to image recognition software, which compares the status of many thousands of objects on sitesuch as electrical sockets and bathroom fittingswith a digital replica of the building.

The AI also uses the video feed to track where the camera is in the building to within a few centimeters so that it can identify the exact location of the objects in each frame. The systemcan track the status of around 150,000 objects several times a week, says Danon. For each object the AI can tell which of three or four states it is in, from not yet begun to fully installed.

Site inspections are slow and tedious, says Sophie Morris at Buildots, a civil engineer who used to work in construction before joining the company. The Buildots AI gets rid of many repetitive tasks and lets people focus on important decisions. That's the job people want to be doingnot having to go and check if the walls have been painted or if someones drilled too many holes in the ceiling, she says.

Another plus is the way the tech works in the background. It captures data without the need to walk the site with spreadsheets or schedules, says Glen Roberts, operations director at Wates. He says his firm is now planning to roll out the Buildots system at other sites.

Comparing the complete status of a project with its digital plan several times a week has also made a big difference during the covid-19 pandemic. When construction sites were shut down to all but the most essential on-site workers, managers on several Buildots projects were able to keep tabs on progress remotely.

But AI wont be replacing those essential workers anytime soon. Buildings are still built by people. At the end of the day, this is a very labor-driven industry, and that won't change, says Morris.

Change note: we have changed the text to clarify how the Buildots system differs from others.

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AI that scans a construction site can spot when things are falling behind - MIT Technology Review

4 AI Stocks That Will Surge in 2021 as Artificial Intelligence Takes Hold – Investorplace.com

Artificial intelligence (AI) is creeping into our everyday lives, often without us realizing it. Today, AI can be found in the digital assistants we use such as Apples (NASDAQ:AAPL) Siri and Amazons (NASDAQ:AMZN) Alexa to check our schedules and search for things on the internet; in the cars we own that now park themselves as they are able to recognize space around the vehicle; and in the small robots we use to clean our houses, such as the Roomba vacuum.

Artificial intelligence is becoming more a part of our lives all the time, and will only grow in importance in coming years.

In the not too distant future, AI will influence everything from how we shop for groceries to how diseases are diagnosed and treated by doctors. It all adds up to a fast growing market. Conservative estimates peg spending on AI software will top $125 billion by 2025 as organizations integrate AI and machine learning into their business processes.

In this article we look at four leading artificial intelligence stocks that are likely to surge in 2021 and beyond as the future is built around us.

Source: Laborant / Shutterstock.com

IBMs research division has long been a leader in developing artificial intelligence. The companys most famous AI creation is its Watson computer. Named after IBM founder, industrialist Thomas J. Watson, the supercomputer can answer questions that are posed to it in plain language.

In 2011, Watson famously competed on the Jeopardy! quiz show against two of the programs greatest champions (Ken Jennings and Brad Rutter) and won. Today, Watson-based programs are now being used across a range of industries from helping to diagnose patients in hospitals, to forecasting the weather, preparing taxes and developing advertisements that resonate with consumers.

Earlier in October, IBM made a major announcement that the 109-year old company will break itself up so that management can focus more on cloud computing and developing artificial intelligence solutions under its Watson brand. The break-up will see IBMs mainstay IT infrastructure services unit spun off into a new, as yet unnamed, company while IBM narrows its focus on cloud computing and AI products and services.

The decision was warmly received by Wall Street. IBM stock jumped 10% on the news. IBM shareholders are poised to reap even more growth over the next year once the company successfully transitions to making AI the center of its business. The median price target on the stock is $140 a share over the coming 12-months.

Source: Benny Marty / Shutterstock.com

Alphabet is more than its ubiquitous search engine Google. The Mountain View, California-based company is involved in everything from healthcare and smart phones to self-driving cars and drones. Alphabet today is quite a diverse business.

One of its main divisions is DeepMind, which focuses on developing artificial intelligence and adding it across Google products. DeepMind has been employing AI to improve items we use everyday such as Google Maps and the Google Nest smart home hub. DeepMind has also contributed to the Alphabets development of self-driving cars and wearable tech such as Alphabets line of smartwatches.

Clearly, Google sees AI as being a significant part of its and our futures. Of course, Alphabet still derives the vast majority of its revenue from online advertising. But the company is using those funds to invest in new business lines and develop new ventures.

Artificial intelligence is one of its areas of strategic investment. GOOGL stock endured a correction with the broader technology sector in September, but many analysts now see the share price moving higher. Deutsche Bank recently upgraded its price target on the stock to $2,020 a share, up from a previous price target of $1,975 and nearly 30% above the $1,567 that Alphabet shares are trading at today.

Source: michelmond / Shutterstock.com

Nvidia isnt just using artificial intelligence, it is creating it. The Santa Clara, California-based company just announced that it will be powering the worlds fastest AI supercomputer that will be based in Europe and called Leonardo. The worlds fastest computer is expected to be involved in drug discovery, space exploration and weather modelling around the world. And powering it all will be Nvidias Ampere-based graphics cards and Mellanox HDR 200 GB networking system.

Besides the Leonardo super computer undertaking, Nvidia is also using AI that it developed to improve video conferences, sharpening images and lessening instances of dropped calls and frozen screens. And the companys graphics chips are being used to power the next generation of video game consoles and cloud-based gaming that is expected to use AI to deepen the gamer experience.

Nvidia is so heavily invested in artificial intelligence that on Oct. 5, company CEO Jensen Huang declared that we are now living in the age of AI, and said AI requires a whole reinvention of computing, full-stack rethinking, from chips to systems, algorithms, tools, the ecosystem.

Nvidia has been on an acquisition spree this year, buying companies that can help it advance its AI capabilities. Companies such as Mellanox Technologies, and its more recent $40 billion bid for ARM Holdings. Everything the company has been doing is getting applause from investors.

NVDA stock is up 185% since its March low and now trades at $553 a share. Analysts see nothing but upside ahead. The median price target of 35 analysts who cover Nvidia is for the companys stock to reach $590 a share within 12-months. Some analysts see the stock hitting $700 per share.

Source: StreetVJ / Shutterstock.com

China is a major player (and U.S. rival) in artificial intelligence. So we would be remiss if we didnt include a Chinese leader in AI on this list. And Tencent gets the nod.

In 2016,Tencent opened an artificial intelligence laboratory in Shenzhen where it is based with a vision to make AI everywhere. Today, the company is developing and perfecting machine learning, speech recognition, natural language processing and computer advancements all in an effort to create practical AI applications in the areas of content, online games, social media and cloud services.

The company is also deploying its AI to video games and healthcare, recently announcing new noise reduction technology for cochlear implants that enable hearing impaired people to hear more clearly in loud environments.

While Tencent and its stock have gotten caught up in the geopolitical fight over technology thats been taking place between China and the U.S., and, to a lesser extent, between China and the European Union, TCEHY stock is nevertheless worth considering, especially for investors who want exposure to Chinas fast growing economy and technology.

Tencent shares are up 65% since March and now trade just shy of $73 a share. Analysts covering the company see the share price rising another 7% to 10% in the coming 12-months. If Tencent continues to lead in the AI space, its share price could outperform in 2021.

On the date of publication, Joel Baglole held shares of AAPL and NVDA.

Joel Baglole has been a business journalist for 20 years. He spent five years as a staff reporter at The Wall Street Journal, and has also written for The Washington Post and Toronto Star newspapers, as well as financial websites such as The Motley Fool and Investopedia.

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4 AI Stocks That Will Surge in 2021 as Artificial Intelligence Takes Hold - Investorplace.com

IoT trends continue to push processing to the edge for artificial intelligence (AI) – Urgent Communications

As connected devices proliferate, new ways of processing have come to the fore to accommodate device and data explosion.

For years, organizations have moved toward centralized, off-site processing architecture in the cloud and away from on-premises data centers. Cloud computing enabled startups to innovate and expand their businesses without requiring huge capital outlays on data center infrastructure or ongoing costs for IT management. It enabled large organizations to scale quickly and stay agile by using on-demand resources.

But as enterprises move toward more remote models, video-intensive communications and other processes, they need an edge computing architecture to accommodate data-hogging tasks.

These data-intensive processes need to happen within fractions of a second: Think self-driving cars, video streaming or tracking shipping trucks in real time on their route. Sending data on a round trip to the cloud and back to the device takes too much time. It can also add cost and compromise data in transit.

Customers realize they dont want to pass a lot of processing up to the cloud, so theyre thinking the edge is the real target, according to Markus Levy, head of AI technologies at NXP Semiconductors, in a piece on therise of embedded AI.

In recent years, edge computing architecture has moved to the fore, to accommodate the proliferation of data and devices as well as the velocity at which this data is moving.

To read the complete article, visit IoT World Today.

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IoT trends continue to push processing to the edge for artificial intelligence (AI) - Urgent Communications

Pimloc gets $1.8M for its AI-based visual search and redaction tool – TechCrunch

U.K.-based Pimloc has closed a 1.4 million (~$1.8 million) seed funding round led by Amadeus Capital Partners. Existing investor Speedinvest and other unnamed shareholders also participated in the round.

The 2016-founded computer vision startup launched a AI-powered photo classifier service called Pholio in 2017 pitching the service as a way for smartphone users to reclaim agency over their digital memories without having to hand over their data to cloud giants like Google.

It has since pivoted to position Pholio as a specialist search and discovery platform for large image and video collections and live streams (such as those owned by art galleries or broadcasters) and also launched a second tool powered by its deep learning platform.This product, Secure Redact, offers privacy-focused content moderation tools enabling its users to find and redact personal data in visual content.

An example use case it gives is for law enforcement to anonymize bodycam footage so it can be repurposed for training videos or prepared for submitting as evidence.

Pimloc has been working with diverse image and video content for several years supporting businesses with a host of classification, moderation and data protection challenges (image libraries, art galleries, broadcasters and CCTV providers), CEO Simon Randall tells TechCrunch.

Through our work on the visual privacy side we identified a critical gap in the market for services that allow businesses and governments to manage visual data protection at scale on security footage. Pimloc has worked in this area for a couple of years building capability and product; as a result, Pimloc has now focused the business solely around this mission.

Secure Redact has two components: A first (automated) step that detects personal data (e.g. faces, heads, bodies) within video content. On top of that is what Randall calls a layer of intelligent tools letting users quickly review and edit results.

All detections and tracks are auditable and editable by users prior to accepting and redacting, he explains, adding: Personal data extends wider than just faces into other objects and scene content, including ID cards, tattoos, phone screens (body-worn cameras have a habit of picking up messages on the wearers phone screen as they are typing, or sensitive notes on their laptop or notebook).

One specific user of redaction with the tool he mentions is the University of Bristol. There, a research group, led by Dr Dima Damen, an associate professor in computer vision, is participating in an international consortium of 12 universities which is aiming to amass the largest data set on egocentric vision and needs to be able to anonymise the video data set before making it available for academic/open source use.

On the legal side, Randall says Pimloc offers a range of data processing models thereby catering to differences in how/where data can be processed. Some customers are happy for Pimloc to act as data processor and use the Secure Redact SaaS solution they manage their account, they upload footage and can review/edit/update detections prior to redaction and usage. Some customers run the Secure Redact system on their servers where they are both data controller and processor, he notes.

We have over 100 users signed up for the SaaS service covering mobility, entertainment, insurance, health and security. We are also in the process of setting up a host of on-premise implementations, he adds.

Asked which sectors Pimloc sees driving the most growth for its platform in the coming years, he lists the following:smart cities/mobility platforms (with safety/analytics demand coming from the likes of councils, retailers, AVs); the insurance industry, which he notes is capturing and using an increasing amount of visual data for claims and risk monitoring and thus looking at responsible systems for data management and processing; video/telehealth, with traditional consultations moving into video and driving demand for visual diagnosis; and law enforcement, where security goals need to be supported by visual privacy designed in by default (at least where forces are subject to European data protection law).

On the competitive front, he notes that startups are increasingly focusing on specialist application areas for AI arguing they have an opportunity to build compelling end-to-end propositions which are harder for larger tech companies to focus on.

For Pimlock specifically he argues it has an edge in its particular security-focused niche given deep expertise and specific domain experience.

There are low barriers to entry to create a low-quality product but very high technical barriers to create a service that is good enough to use at scale with real in the wild footage, he argues, adding: The generalist services of the larger tech players do not match up with domain specific provisions of Pimloc/Secure Redact. Video security footage is a difficult domain for AI, systems trained on lifestyle/celebrity or other general data sets perform poorly on real security footage.

Commenting on the seed funding in a statement, Alex van Someren, MD of Amadeus Capital Partners, said: There is a critical need for privacy by design and large-scale solutions, as video grows as a data source for mobility, insurance, commerce and smart cities, while our reliance on video for remote working increases. We are very excited about the potential of Pimlocs products to meet this challenge.

Consumers around the world are rightfully concerned with how enterprises are handling the growing volume of visual data being captured 24/7. We believe Pimloc has developed an industry leading approach to visual security and privacy that will allow businesses and governments to manage the usage of visual data whilst protecting consumers. We are excited to support their vision as they expand into the wider Enterprise and SaaS markets, added Rick Hao, principal at Speedinvest, in another supporting statement.

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Pimloc gets $1.8M for its AI-based visual search and redaction tool - TechCrunch

Companies Work on AI-Based Sensors, Weapons for Use in Image Processing, Target Identification – ExecutiveBiz

artificial intelligence

Defense contractors are working on artificial intelligence-powered sensors and other partially autonomous machines that could help the U.S. Army process images and identify targets, Breaking Defense reported Thursday.

Vern Boyle, vice president for advanced capabilities at Northrop Grumman, said companies are developing sensors that can identify features and share data with other systems without requiring a lot of command and control back into physical systems.

An example of a weapon system that can see, share and record data is the Ripsaw robotic tank demonstrator from Textron Systems, Howe & Howe and FLIR Systems. This combat vehicle features a Skyraider quadcopter drone and a ground robot.

The quality of image processing by sensors and other machines relies on the quality of collected data and industry executives said companies should train algorithms on weird images and data to ensure their accuracy in target identification.

Should we bias training data towards the weird stuff?, said Patrick Biltgen of Perspecta. If theres a war, were almost certain to see weird things weve never seen before.

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Companies Work on AI-Based Sensors, Weapons for Use in Image Processing, Target Identification - ExecutiveBiz

Facebook to use artificial intelligence in bid to improve renewable energy storage – CNBC

Facebook and Carnegie Mellon University have announced they are trying to use artificial intelligence (AI) to find new "electrocatalysts" that can help to store electricity generated by renewable energy sources.

Electrocatalysts can be used to convert excess solar and wind power into other fuels, such as hydrogen and ethanol, that are easier to store. However, today's electrocatalysts are rare and expensive, with platinum being a good example, and finding new ones hasn't been easy as there are billions of ways that elements can be combined to make them.

Researchers in the catalysis community can currently test tens of thousands of potential catalysts a year but Facebook and Carniegie Mellon believe they can increase the number to millions, or even billions, of catalysts with the help of AI.

The social media giant and the university on Wednesday released some of their own AI software "models" that can help to find new catalysts but they want other scientists to have a go as well.

To support these scientists, Facebook and Carnegie Mellon have released a data set with information on potential catalysts that scientists can use to create new pieces of software.

Facebook said the "Open Catalyst 2020" data set required 70 million hours of compute time to produce. The data set includes "relaxation" calculations for a million possible catalysts as well as supplemental calculations.

Relaxations, a widely used measurement in catalysis, are calculated to see if a particular combination of elements will make a good catalyst.

Each relaxation calculation, which simulates how atoms from different elements will interact, takes scientists around eight hours on average to work out, but Facebook says AI software can potentially do the same calculations in under a second.

If you study catalysis, "that's going to dramatically change how you do your work and how you do your research," said Larry Zitnick, a research scientist at Facebook AI Research, on a call ahead of the announcement.

In recent years, tech giants like Facebook and Google have attempted to use AI to speed up scientific calculations and observations across multiple fields.

For example, DeepMind, an AI-lab owned by Google parent Alphabet, developed AI software capable of spotting tumors in mammograms faster and more accurately than human researchers.

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Facebook to use artificial intelligence in bid to improve renewable energy storage - CNBC

The grim fate that could be ‘worse than extinction’ – BBC News

Toby Ord, a senior research fellow at the Future of Humanity Institute (FHI) at Oxford University, believes that the odds of an existential catastrophe happening this century from natural causes are less than one in 2,000, because humans have survived for 2,000 centuries without one. However, when he adds the probability of human-made disasters, Ord believes the chances increase to a startling one in six. He refers to this century as the precipice because the risk of losing our future has never been so high.

Researchers at the Center on Long-Term Risk, a non-profit research institute in London, have expanded upon x-risks with the even-more-chilling prospect of suffering risks. These s-risks are defined as suffering on an astronomical scale, vastly exceeding all suffering that has existed on Earth so far. In these scenarios, life continues for billions of people, but the quality is so low and the outlook so bleak that dying out would be preferable. In short: a future with negative value is worse than one with no value at all.

This is where the world in chains scenario comes in. If a malevolent group or government suddenly gained world-dominating power through technology, and there was nothing to stand in its way, it could lead to an extended period of abject suffering and subjugation. A 2017 report on existential risks from the Global Priorities Project, in conjunction with FHI and the Ministry for Foreign Affairs of Finland, warned that a long future under a particularly brutal global totalitarian state could arguably be worse than complete extinction.

Singleton hypothesis

Though global totalitarianism is still a niche topic of study, researchers in the field of existential risk are increasingly turning their attention to its most likely cause: artificial intelligence.

In his singleton hypothesis, Nick Bostrom, director at Oxfords FHI, has explained how a global government could form with AI or other powerful technologies and why it might be impossible to overthrow. He writes that a world with a single decision-making agency at the highest level could occur if that agency obtains a decisive lead through a technological breakthrough in artificial intelligence or molecular nanotechnology. Once in charge, it would control advances in technology that prevent internal challenges, like surveillance or autonomous weapons, and, with this monopoly, remain perpetually stable.

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The grim fate that could be 'worse than extinction' - BBC News

NVIDIA Releases a $59 Jetson Nano 2GB Kit to Make AI More Accessible to Developers – InfoQ.com

NVIDIA recently debuted the Jetson Nano 2GB developer kit. For $59, the kit includes a credit-card-sized single-board computer with a quad-core ARM CPU and a 128 core Maxwell GPU. It comes with the JetPack SDK, a Ubuntu Linux-based developer SDK, and comprehensive documentation. The Jetson Nano 2GB kit also includes online training and certification, making it an ideal developers kit for students and new developers who just started AI programming.

Deep learning is one of the most notable advancements in computer science in the past decade. Deep learning-based AI applications are now used everywhere, from computer vision to speech to natural language processing. However, as developers, learning AI skills for professional programming has been difficult. The barrier is the GPU. Most deep learning algorithms and frameworks are designed to run on the GPU. They are too slow on the CPU. Yet, most computers are CPU-based. While a personal computer typically contains a GPU to drive graphics, the operating system and software stack in the computer are designed to "hide" the GPU and only use it to drive the display graphics.

When a developer writes a deep learning application and runs it on a personal or cloud-based computer, there is a good chance the program is actually running on CPUs. With the Jetson series of devices and software SDKs, NVIDIA creates a coherent development environment to learn and develop GPU-based AI applications. The JetPack SDK provides a customized version of eLinux, which is based on and compatible with Ubuntu 18.04, and curated versions of key software packages, such as Python, TensorFlow, PyTorch, Numpy, OpenCV, to ensure that the whole software stack is optimized for the GPU and ARM CPU hardware on the development board. In addition to official learning resources from NVIDIA, there is a vibrant community of developers and hobbyists in the Jetson ecosystem. There is a wealth of YouTube videos, open-source projects, and online articles for these devices.

The entry-level Jetson device, called the Jetson Nano, was priced at $99, which is a little high compared with other single board computers such as the Raspberry Pi, which is priced at $40 without the GPU. The new Jetson Nano 2GB's $59 price point is much more reasonable for price-sensitive students and hobbyists learning AI programming.

Like the regular Jetson Nano, the Jetson Nano 2GB has a 64-bit quad-core ARM A57 CPU clocked at 1.43 GHz, and a 128 CUDA core Maxwell GPU. The GPU delivers 472 GFLOPS computing power for AI applications. In fact, for AI applications, the Jetson Nano 2GB is 8 to 73 times faster than the most advanced Raspberry Pi 4.

The Jetson Nano 2GB board has several USB 2/3 connectors, a power connector, an HDMI display connector, an ethernet connector, GPIO pins, a camera kit connector, as well as an M2 key E connector for a WiFi and Bluetooth card. The 2GB refers to the on-board memory space. You do need an additional microSD card for the operating system and files in order to boot up and use the Jetson Nano 2GB. Furthermore, you will need to connect the card to an HDMI display, keyboard, and mouse, as well as the network (either cable-based Ethernet or WiFi card) before you can use it as a computer.

With its small size and low cost, the Jetson Nano 2GB can power computer vision applications in robots or drones. Its GPUs can analyze video streams from the camera in real-time, recognize objects and faces in each video frame, and send out corresponding control commands through its GPIO pins or USB connectors. However, with the 10W power consumption of the GPU working in full video image recognition mode, it is also challenging to keep the device running on batteries for an extended period of time. Therefore, the Jetson Nano 2GB is indeed primarily a learning device.

You can pre-order the Jetson Nano 2GB from online retailers, and it should be available in late October. Follow the learning resources and tutorials from the NVIDIA website to start programming AI applications on your GPU!

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NVIDIA Releases a $59 Jetson Nano 2GB Kit to Make AI More Accessible to Developers - InfoQ.com

Top tech trends for 2021: Gartner predicts hyperautomation, AI and more will dominate business technology – TechRepublic

Operational resiliency is key as the COVID-19 pandemic continues to change how companies will do business next year.

There are nine top strategic technology trends that businesses should plan for in 2021 as the pandemic continues, according to Gartner's analysts. Their findings were presented on Monday at the virtual Gartner IT Symposium/Xpo Americas conference, which runs through Thursday.

Organizational plasticity is key to these trends. "When we talk about the strategic technology trends, we actually have them grouped into three different themes, which is people centricity, location independence, and resilient delivery," said Brian Burke, research vice president at Gartner. "What we're talking about with the trends is how do you leverage technology to gain the organizational plasticity that you need to form and reform into whatever's going to be required as we emerge from this pandemic?"

SEE: COVID-19 workplace policy (TechRepublic Premium)

Here are the top nine trends, in no particular order. And they will have an impact for more than the next year. Companies can look at these for insight through 2025, per Gartner.

"We don't prioritize these. So we don't say that one is more important than the other," Burke explained. "Different organizations in different industries will prioritize the impact of the trends on them as being higher or lower, but when we look really across industries and across geographies and across these trends, we think that these are the most impactful trends that organizations generally are going to face over the next five years."

The Internet of Behaviors (IoB) is an emerging trend. The term "Internet of Behaviors" was first coined inGartner's tech predictions for 2020. This is how organizations, whether government or private sector, are leveraging technology to monitor behavioral events and manage the data to upgrade or downgrade the experience to influence those behaviors. This is what Gartner calls the "digital dust" of peoples' daily lives. It includes facial recognition, location tracking, and big data.

Burke said, "In practical terms, it's real things like health insurance companies that are monitoring your fitness bands and your food intake, and the number of times you go to the gym, and those things to adjust your premiums."

Gartner predicts that by the end of 2025, more than half of the world's population will be subject to at least one IoB program. Burke said: "That might be a little bit of an understatement because when you think about the social credit system in China, you're already up to double digit percentages of people that are being monitored just with one implementation. There's all kinds of these things that are popping up here and there and everywhere."

The cybersecurity mesh technology trend enables people to access any digital asset security, no matter where the asset is, or where the person is located. Burke said: "The cybersecurity mesh is really how we've really reached a tipping point or inflection point with security, and that's causing us to really decouple policy enforcement from policy decision-making. Those were coupled in the past. What that allows us to do is it allows us to put the security perimeter around the individual as opposed to around the organization."

He added "The way that security professionals have traditionally thought about security is that inside of the organization is secure. Then we make sure that everything outside of your organization is secured through that security mechanism inside the organization, inside the firewall, so to speak."

With more digital assets outside of the firewall, particularly with cloud and more remote employees, the security perimeter needs to be around an individual and enforcement is handled through a cloud access security broker, so that policy enforcement is done at the asset itself, Burke explained.

Gartner predicts that by 2025, the cybersecurity mesh will support more than half of digital access control requests.

Another trend is total experience (TX). Last year, Gartner introduced multiexperience and this is a step beyond that. Multiexperience is multiple modes of access using different technologies, and TX ties together customer experience, employee experience, and user experience with the multiexperience environment, Burke said.

Organizations need a TX strategy as interactions become more mobile, virtual, and distributed, particularly as a result of the COVID-19 pandemic.

"The challenge is that in most organizations, those different disciplines are siloed. So what we're saying the basis of that prediction is that if you can bring together customer experience, employee experience, multi experience and user experience, the common notarial effect, common notarial innovation as a combination of strategies is harder to replicate than in a single strategy, according to Michael Porter. And we believe that, too. So you can bring those things together. That's where you'll gain the competitive advantage that will be realized through those experience metrics," Burke said.

Gartner predicts that organizations providing a TX will outperform competitors across key satisfaction metrics over the next three years.

This trend, intelligent composable business, is about leveraging from an application perspective and leveraging packaged business capabilities, which can be thought of as chunks of functionality accessible through APIs, Burke said.

"They can be developed by vendors or provided by vendors or developed in-house. That kind of framework, that allows you to cobble together those package business capabilities, and then access data through a data fabric to provide it's configuration and rapid reconfiguration of business services that can be highly granular even personal acts."

"The intelligent composable business is about bringing together things like better decision making, better access to data that changes the way that we do things, which is required for flexible applications, and which we can deliver when we have this composable approach to application delivery," Burke said.

Hyperautomation is another key strategic trend for 2021. It was a top strategic trend last year as well, and it has been evolving.

"We've seen tremendous demand for automating repetitive manual processes and tasks; so robotic process automation was the star technology that companies were focused on to do that. That has been happening for a couple of years, but what we're seeing now is that it's moved from task based automation, to process based automation, so automating a number of tasks in a process, to functional automation across multiple processes and even moving towards automation at the business ecosystem level. So really, the breadth of automation has expanded as we go forward with hyperautomation," Burke explained.

Another strategic trend, anywhere operations, refers to an IT operating model that supports customers everywhere and enables employees everywhere and manages the deployment of business services across distributed infrastructure.

Burke said that anywhere operations were always there but the pandemic made urgent.

"There always had been a movement towards location independent and providing services at the point where they're required. But back at least in America and Europe in March, suddenly all of these people working from home really raised the awareness of it, which was we have an immediate need to be able to support remote employees and most organizations were able to resolve that really quickly. But then we also are dealing with our customers, and our customers are remote and our products need to become a deliverable remotely as well."

With employees working from home, and salespeople working from home, talking to purchasing agents and buyers working from home, it ramped up the problem and the need to deliver services to people wherever they are and wherever they are required, Burke said.

Gartner predicts that by the end of 2023, 40% of organizations will have applied anywhere operations to deliver optimized and blended virtual and physical customer and employee experiences.

This trend involves providing engineering discipline to an organization because only 53% of projects make it from artificial intelligence (AI) prototypes to production, according to Gartner research.

"AI engineering is about providing the sort of engineering discipline, a robust structure that will emphasize having AI projects that are delivered in a consistent way to ensure that they can scale, move into production, all of those kinds of things. So it's really bringing the engineering discipline to AI for end user organizations. So when you talk about large vendors, yes, they've been delivering successfully for the past quite a few years, but end user organizations are needing to move out of the experimental stage with AI and move into a robust delivery model and that's really what AI engineering's about," Burke said.

Distributed cloud is another technology trend and it involves the distribution of public cloud services to different physical locations while the operation, governance, and evolution of the services are the responsibility of the public cloud provider.

Gartner predicts that by 2025, most cloud service platforms will provide at least some distributed cloud services that begin at the point of need.

Privacy is more important than ever as global data protection legislation matures, and Gartner predicts that by 2025, half of large organizations will implement privacy-enhancing computation for processing data in untrusted environments and multiparty data analytics use cases. Privacy-enhancing computation protects data in use while maintaining secrecy or privacy.

Burke said the actual number of companies that will use privacy-enhancing computation is tough to assess. "That's a difficult one to gauge because what we've seen over the years of course, is that a lot of organizations have not focused as much attention as they probably require on privacy. But we think that what's happening now is that privacy legislation globally is really starting to take hold. So when privacy legislation is introduced, it takes a while for enforcement to catch up to legislation."

He added: "Privacy is going to be an issue for organizations going forward. The importance is going to increase, but also the opportunities are going to be increased to be able to use trusted third parties for analytics and share data across priorities without exposing the private details in that data and that kind of thing."

"One of the things that's really an underlying premise of all of our research, including the top technology trends, is that we're not going to come out of the pandemic and go back to what we were," Burke said. "We're going to come out of the pandemic, but we're going to move forward on a different trajectory. So really, trying to anticipate what that trajectory is going to be for your organization helps to guide you on how you're going to emerge from the pandemic on that different trajectory. So these trends are focused on organizational agility because that's what's going to be successful as we step into a new future phase, hopefully sometime soon."

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AI engineering is one of the key strategic trends Gartner predicts for 2021.

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Top tech trends for 2021: Gartner predicts hyperautomation, AI and more will dominate business technology - TechRepublic

Artificial Intelligence Cold War on the horizon – POLITICO

While the U.S. has lacked central organizing of its AI, it has an advantage in its flexible tech industry, said Nand Mulchandani, the acting director of the U.S. Department of Defense Joint Artificial Intelligence Center. Mulchandani is skeptical of Chinas efforts at civil-military fusion, saying that governments are rarely able to direct early stage technology development.

Tensions over how to accelerate AI are driven by the prospect of a tech cold war between the U.S. and China, amid improving Chinese innovation and access to both capital and top foreign researchers. Theyve learned by studying our playbook, said Elsa B. Kania of the Center for a New American Security.

Many commentators in Washington and Beijing have accepted the fact that we are in a new type of Cold War, said Ulrik Vestergaard Knudsen, deputy secretary general of Organization for Economic Cooperation and Development (OECD), which is leading efforts to develop global AI cooperation. But he argued that we should not abandon hope of joining forces globally. Leading democracies want to keep the door open: Ami Appelbaum, chairman of Israels innovation authority, said we have to work globally and we have to work jointly. I wish also the Chinese and the Russians would join us. Eric Schmidt said coalitions and cooperation would be needed, but to beat China rather than to include them. "China is simply too big," he said. "There are too many smart people for us to do this on our own."

The invasive nature and the scale of many AI technologies mean that companies could be hindered in growing civilian markets, and the public could be skeptical of national security efforts, in the absence of clear frameworks for protecting privacy and other rights at home and abroad.

A Global Partnership on AI (GPAI), started by leaders of the Group of Seven (G7) countries and now managed by the OECD, has grown to include 13 countries including India. The U.S. is coordinating an AI Partnership for Defense, also among 13 democracies, while the OECD published a set of AI Principles in 2019 supported by 43 governments.

Knudsen said that it is important for AI global cooperation to move cautiously. Multilateralism and international cooperation are under strain, he said, making a global agreement on AI ethics difficult. But if you start with soft law, if you start with principles and let civil society and academics join the discussion, it is actually possible to reach consensus, he said.

Data and cultural dividing lines

Major divisions exist over how to handle data generated by AI processes. In Europe, we say that its the individual that owns the data. In China, its the state or the party. And then theres a divide in the rest of the world, said Knudsen. There is a right to privacy that accrues to everyone, according to Courtney Bowman, director of privacy and civil liberties engineering at data-mining and surveillance company Palantir Technologies. But we have to recognize that privacy does have a cultural dimension. There are different flavors, he said.

Most experts agree there is the scope to regulate how data is used in AI. Palantirs Bowman says that AI success isnt about unhindered access to the biggest datasets. To build competent, capable AI its not just a matter of pure data accumulation, of volume. It comes down to responsible practices that actually align very closely with good data science, he said.

The countries that get the best data sets will develop the best AI: no doubt about it, said Nand Mulchandani. But he said that partnerships are the way to get that data. Global partnerships are so incredibly important because they give access to global data, which in aggregate is better than even a huge dataset from within a single country such as China.

How can government boost AI?

Rep. Cathy McMorris Rodgers (R - WA) , a leading Republican voice on technology issues, wants the U.S. government to create a foundation for trust in domestic AI via measures such as a national privacy standard. We need to be putting some protections in place that are pro-consumer so that there will be trust, in pro-American technology, she said.

U.S. Rep. Pramila Jayapal (R-Wa.) wants both government regulation and private sector standards while AI technologies particularly facial recognition are still young. The thing about technology is, once it's out of the bottle, it's out of the bottle, she said. You can't really bring back the rights of [Michigan resident Robert Williams who was arrested based on a faulty ID by facial recognition software], or the rights of Uighurs in China, who are bearing the brunt of this discriminatory use of facial recognition technology. Some experts argue that while regulation is needed, it must be sector-specific, because AI is not a single concept, but a family of technologies, with each requiring a different regulatory approach.

Government has a role in making data widely available for the development of AI, so that smaller companies have a fair opportunity to research and innovate, said Charles Romine, Director of the Information Technology Laboratory (ITL) within the National Institute of Standards and Technology (NIST).

On the question of government AI funding, Elsa Kania said that its not possible to make direct comparisons between U.S. and Chinese government investments. The U.S. has more venture capital, for example, while eye-popping investment figures from Chinas central government dont mean an awful lot if they arent matched by investments in talent and education, she said. We shouldnt be trying to match China dollar-for-dollar if we can be investing smarter.

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Artificial Intelligence Cold War on the horizon - POLITICO

Go Beyond Artificial Intelligence: Why Your Business Needs Augmented Intelligence – Forbes

Augmented Intelligence

The nasal test for Covid-19 requires a nurse to insert a 6-inch long swab deep into your nasal passages. The nurse inserts this long-handled swab into both of your nostrils and moves it around for 15 seconds.

Now, imagine that your nurse is a robot.

A few months ago, a nasal swab robot was developed by Brain Navi, a Taiwanese startup. The companys intent was to minimize the spread of infection by reducing staff-patient contact. So, here we have a robot autonomously navigating the probe down into your throat, and carefully avoiding channels that lead up to the eyes.

The robot is supposed to be safe. But many patients would, understandably, be terrified.

Unfortunately, enterprise applications of artificial intelligence (AI) are often no less misguided. Today, AI has picked up remarkable capabilities. Its better than humans in tasks such as voice and image recognition, across disciplines from audio transcription to games.

But does this mean we should simply hand over the reins to machines and sit back? Not quite.

You need humans to make your AI solutions more effective, acceptable, and humane for your users. Thats when they will be adopted and deliver ROI for your organization. When AI and humans combine forces, the whole can be greater than the sum of its parts.

This is called augmented intelligence.

Here are 4 reasons why you need augmented intelligence to transform your business:

A large computer manufacturer wanted to find out what made its customers happy. Gramener, a company providing data science solutions analyzed tens of thousands of comments from the clients bi-annual voice of customer (VoC) survey. A key step in this text analytics process was to find what the customers were talking about. Were they worried about billing or after-sales service?

The team used AI language models to classify comments into the right categories. The algorithm delivered an average accuracy of over 90%, but the business users werent happy. While the algorithm aced at most categories, there were a few where it stumbled, at around 60% accuracy. This led to poor decisions in those areas.

Algorithms perform best when they are trained on large volumes of data, with a representative variety of scenarios. The low-accuracy categories in this project had neither. The project team experimented by bringing in humans to handle those categories where the models confidence was low.

At low manual effort, the overall solution accuracy shot up. This delivered an improvement of 2 percentage points in the clients Net Promoter Score.

Algorithms detect online fraud by studying factors such as consumer behavior and historical shopping patterns. They learn from past examples to identify whats normal and whats not. With the onset of the pandemic, these algorithms started failing.

In todays new normal, consumers have gone remote. They spend more time online, and the spending patterns have shifted in unexpected ways. Suddenly, everything these algorithms have learned has become irrelevant. Covid-19 threw them a curveball.

Algorithms work well only in scenarios that they are trained for. In completely new situations, humans must step in. Organizations that have kept humans in the loop can quickly transition control to them in such situations. Humans can keep systems running smoothly by ensuring that they are resilient in the face of change.

Meanwhile algorithms can go back to the classroom to unlearn, relearn, and come back a little smarter. For example, a recent NIST study found that the use of face masks is breaking facial recognition algorithms, such as the ones used in border crossings. Most systems had error rates up to 50%, calling for manual intervention. The algorithms are being retrained to use areas visible around the eyes.

On March 18, 2018, Elaine Herzberg was walking her bike across Mill Avenue. It was around 10 p.m in Tempe, Arizona. She crossed several lanes of traffic, before being struck by a Volvo.

But this wasnt any Volvo. It was a self-driving car, being tested by Uber.

The car was trained to detect jaywalkers at crosswalks. But, Herzberg had been crossing in the middle of the road, so the AI failed to detect her.

This tragic incident was the first pedestrian death caused by a self-driving car. It raised several questions. When AI makes a mistake, who should be held responsible? Is it the carmaker (Volvo), the AI system maker (Uber), the car driver (Rafaela Vasquez), or the pedestrian (Elaine Herzberg)?

Occasionally, high-precision algorithms will falter, even in familiar scenarios. Rather than roll back the advances made in automation, we must make efforts to improve accountability. Last month, the European Commission published recommendations from an independent expert report for self-driving cars.

The experts call for identifying ownership of all parties and for devising ways to attribute responsibility across scenarios. The report recommends an improvement of human-machine interactions so that AI and drivers can communicate better and understand each others limitations.

Will Siri, Alexa or Google Assistant discriminate against you? Earlier this year, researchers at Stanford University attempted to answer this question by studying the top voice recognition systems in the world. They found that these popular devices had more trouble understanding Black people than white people. They misidentified 35 percent of words spoken by Black users, but only 19 percent for white users.

Bias is a thorny issue in AI. But we must remember that algorithms are only as good as the data used to train them. Our world is anything but perfect. When algorithms learn from our data, they mimic these imperfections and magnify the bias. There is ongoing research in AI to improve fairness and ethics. However, no amount of model engineering will make algorithms perfect.

In the real world, if we are serious about fighting bias, we use our judgement. We make rules more inclusive and adopt measures to amplify suppressed voices. The same approach is needed in AI solutions. Design human intervention to check and address potential scenarios of discrimination. Use human judgment to fight a machines learned bias.

Rethink Design

We often measure progress in AI by comparing AIs abilities to that of humans.

While thats a useful benchmarking exercise, its a mistake to use this approach while designing AI solutions. Organizations often pit AI against humans. This doesnt do justice to either one. It leads to suboptimal performance, brittle solutions, untrustworthy applications and unfair decisions.

Augmented intelligence combines the strengths of humans with those of AI. It combines the speed, logic and consistency of machines with the common sense, emotional intelligence and empathy of humans.

To achieve augmented intelligence, you need humans in the loop. This mustbe planned upfront. Merely adding new processes or responsibilities to an existing technology solution leads to poor results. You must (re)design the solution workflow, and decide which areas are best handled by algorithms. You should define whether humans must make decisions or review decisions made by a machine.

Building augmented intelligence is an ongoing journey. With evolution in machine capabilities and changes in users comfort and trust levels, you must continuously improve the design.

This will make AI-driven systems that do invasive medical procedures or that make high-stakes financial decisions more compassionate and trustworthy for your users.

Disclosure: I co-founded the company, Gramener, that's mentioned in one of the examples in this article.

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Go Beyond Artificial Intelligence: Why Your Business Needs Augmented Intelligence - Forbes

Total partners with Google to deploy AI-powered solar energy tool – The Hindu

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French energy company Total has developed a tool to determine solar energys potential of rooftops. Partnering with Google Cloud, the tool will help popularise the deployment of solar energy panels in households.

The tool Solar Mapper uses artificial intelligence (AI) algorithm to extract data from satellite images. AI helps facilitates sharper and quicker estimation of solar energy potential than present tools, the company said in an official statement.

The tool will also guide households to understand what technology would need to be installed depending on solar energy requirements.

Researchers from Total and Google Cloud took 6 months to devise the programme. At present, Solar Mapper is said to provide nearly 90% coverage in France.

Also read | Alphabet's robotic plant buggy can scan crops, gather data

The availability of the tool will expand all through Europe and the rest of the world soon, the team said. Solar Mapper will also expand its application to industrial and commercial buildings.

Total said this will help further its goal of becoming net-zero emission by 2050.

In September, Googles CEO Sundar Pichai said in a video message the company has ended its carbon legacy, making it the first major company to do so.

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Total partners with Google to deploy AI-powered solar energy tool - The Hindu

Our Movement Is Strongest When We Aim HighAnd Work in Current Reality – Filter

Criminal justice reform has made significant advances during my time in the movement. But the continuing influence of the carceral lobby has also inflicted severe setbacks. As we work to create a more just system and society, we have to account and plan not just for the endgame, but for whats occurring in the moment.

As a speaker for the Law Enforcement Action Partnership and its current chair, let me first underline our opposition to police violence and our support for the resulting wave of nonviolent protests across the country.

These reactions to the killings of George Floyd, Breonna Taylor, Ahmaud Arbury and others create opportunities to transform policing. Whats largely missing are the strategic plans that could achieve not only that transformation but one of the entire criminal justice systemby engaging and convincing a majority of the population. Such strategies must anticipate the inevitable blowback from our opponents and those members of the public who support tough-on-crime policiespolicies that continue to be supported by the average Americans lack of understanding of the causes of crime.

Professor Patrick Sharkey made this point in a Washington Post piece about how defunding the police can work. He shared my concern that without adequately scaling and presenting model defunding programs, any ensuing rise in violence will see Americans of all races become more punitive, supporting harsher policing and criminal justice policies. Thats how we got to this point.

Crime data, often used selectively, can easily be deployed to fuel political rhetoric and maintain the status quo.

Because thats how the carceral lobby has always successfully driven home its message. They wont lack opportunity. Just last week, the Washington Post published another article, describing how crime rose unevenly when stay-at-home order lifted. Crime data, often used selectively, can easily be deployed to fuel political rhetoric and maintain the status quo.

There are many recent examples of how pro-carceral groups use the fear of crime to push back against reform. Take California, and the backlash against Proposition 47an initiative, approved by voters in 2014, that was designed to reduce incarceration while reallocating fiscal resources away from police and back into communities to make them safer. Despite the research reflecting that the backlash is unfounded, organizations like Keep California Safe have waged a public campaign against successful criminal reform. The result was the placement of a ballot initiative this election that would roll back gains made since the passage of Proposition 47.

Its not just California. You only need to look at the bail reform rollbacks or the fight to shield police disciplinary records in New York to understand the power and influence of the Police Benevolent Association and police unions across the nation.

Connected with an understanding of the formidable forces ranged against us, we also need to appreciate the reality of where public opinion currently standsand the many nuanced factors that inform these views.

Polling conducted since the death of George Floyd on how Americans feel about defunding the police, including Gallup and the latest Monmouth poll on race relations, reflects these complexities. The political data site 538 averaged four national polls conducted in mid-June, at the height of the protests, finding 31 percent in favor of defunding the police and 58 percent opposed.

In addition to asking the question on defunding, the Gallup poll also found:

When asked whether they want the police to spend more time, the same amount of time or less time than they currently do in their area, most Black Americans61%want the police presence to remain the same. This is similar to the 67% of all US adults preferring the status quo, including 71% of White Americans.

Meanwhile, nearly equal proportions of Black Americans say they would like the police to spend more time in their area (20%) as say theyd like them to spend less time there (19%).

For sure, there are many valid counterpoints to such findings. Public opinion can changesupport for defunding, while well short of a majority, is much higher than in the pastand public opposition should not stop our advocating for whats right. The historical trajectory of public support for marijuana legalization, which rose from 25 percent in the late 1990s to 66 percent today, is a perfect example of how advocacy can change minds.

It may jar with our worldview, but denying that current reality doesnt help us.

We should work optimistically and think big, but I offer two cautions. First, that public support for reforms can go down as well as upjust as approval for marijuana legalization, having peaked at 28 percent in 1977 (according to Pew), fell off as the War on Drugs kicked in, then took two decades to recover to that level. And second, that we do need to do the hard work of persuading people.

It may jar with our worldview that even communities marginalized by systemic racism and policing practices just want good policing, not police abolition or defunding, but denying that current reality doesnt help us. Successful advocacy may take many years. If that proves to be the case with the national-scale establishment of non-law enforcement responses to crime, it is imperative that we achieve other things in the meantime.

We can both develop and advocate for sweeping policy changes, while simultaneously recognizing that even incremental reforms have made and can make crucial differences in peoples lives.

My own work in harm reduction and drug policy reflects this dynamic tension between how we plan for the ideal future while also addressing immediate needs of our communities by working within the politics of now. Reform is not linear, transforming an entrenched system rooted in punishment will not happen overnight, and we ignore public opinion at our peril.

How can we convince people who may fear the radical transformations we envisage? One important way is by demonstrating to them how unthreatening steps in this direction can beby designing and implementing policies that scale up non-law enforcement first responder resources while scaling down police budgets and reach. Proven successes of such measures, including enhanced community safety, can become winning arguments as we seek to advance further.

Change of this nature will require deconstructing the current system piece by piece, and starting in the areas where we already have consensus is logical.

We have to take the public with us every step of the wayby seeking inclusive community input on central questions like What are the Police for? and by developing consensus on the meaning of public safety and who should be responsible for it. I would suggest framing this by adopting the term community-led health and safetya concept that views crime reduction through a multi-disciplinary lens, centering the interdependence of social, cultural and socioeconomic factors on health and opportunity, as well as crime.

The dismantling and rebuilding of policing and its alternatives around community values and under community leadershipin ways that invest in people, not a system of punishment and abandonmentis a clear but complex goal, and one whose outcomes will look different in different contexts. Change of this nature will require deconstructing the current system piece by piece, and starting in the areas where we already have consensus is logical.

One area in which Americans have widely agreed for years is the failure of our drug policy. A 2012 Angus Reid poll found that only 10% of respondents believe that the War on Drugs has been a success, while 66% deem it a failure. Majorities of Democrats (63%), Republicans (63%) and Independents (69%) alike agree with the notion that the War on Drugs has not been fruitful. A 2014 Pew Poll showed that 67 percent favored treatment not jail for heroin and cocaine use. And a 2019 CATO poll reflected that 55 percent favor decriminalizing all drugs.

Defunding the drug war is clearly an action that will help us to dismantle policing practices that subvert our constitutional rights, entrench structural racism, corrupt police themselves and destroy the prospects of establishing community-led health and safety structures.

Despite advances like marijuana legalization, sentencing reform, Good Samaritan laws or Oregons current ballot measure to decriminalize drug possession, many politicians have been slow to respond to the public will to pull apart the drug war. Organizations like my own, the Drug Policy Alliance*, the National Harm Reduction Coalition and many others have worked to win these victories and speed these changes. But one program that has for years been making a real difference in the lives of people who use drugs, actually preventing them from being criminalized, is Law Enforcement Assisted Diversion (LEAD).

LEAD essentially works by diverting eligible people who come into contact with police to social services, rather than jail and criminal charges. I have often written about LEAD, admiring the way it has worked, since 2011, within the reality of our current structures to help thousands of criminalized people right now, rather than postponing intervention until we can get laws changed.

I have always recognized it as a forward step, rather than an end goal, and one that should keep evolving.

LEAD is embedded within the very system that we want to changealbeit that is exactly where great harms can be preventedand initially focused on people who use drugs, rather than casting a wider net to include many other marginalized and criminalized groups. For these reasons, I have always recognized it as a forward step, rather than an end goal, and one that should keep evolving.

The architects of LEAD have always recognized this too.

The LEAD framework is all about reducing harm and shifting the paradigm through which our society has responded to marginalized people and vulnerable populations for decades, Chief Brendan Cox, director of policing strategies for the LEAD National Support Bureau and a LEAP speaker, told me. While police were the portal through which enormous numbers were detained and punished, change had to interrupt that flow of people to prisons and courtrooms. Not engaging the police would have abandoned those people.

The evolution that was always anticipated is happening. It has never made sense to condition access to high-quality care on police contact, and LEAD has always been structured to reduce police involvement, Cox said. LEAD has evolved, through partnership with communities across the country, from pre-booking police diversion, to police-centered pre-arrest social contact referrals, to the newly established Let Everyone Advance with Dignity, which allows community members to make direct referrals without any police involvement or approval. Public safety transformation can not happen overnight, yet through this continued shift, LEAD continues to further de-center the criminal legal systems role in providing life changing services to those most in need.

As we continue to reallocate law enforcement resources back to communities and to reduce the gatekeeping role of the police, I would suggest that LEADabove all in its emerging community referrals-based guiseshould eventually replace problematic drug courts, non-coercively offering services to a wide range of people in need.

But above all, were it not for LEAD, countless more lives would have been ruined by convictions, incarceration and criminal records.

So this is where I differ from some, though not all, of the views expressed by Filters Helen Redmond in her recent piece about LEAD. She described LEAD advocates belief in the potential for reconciliation and healing in police-community relations as naive. Yet to pin all our hopes on the rapid implementation of radical structural reform that doesnt currently enjoy majority public support, passing up chances to reduce the harms of the system in the interim, could be described similarly.

Kevin Sabet, the prominent opponent of marijuana legalization, likes to claim that legalizers promised legalization would end racial disparities in arrests. Only, we never said that. Racial disparities in marijuana arrests have sadly continued in many jurisdictions post-legalizationending racism, like transforming the criminal justice system, is a long haul, despite its urgency.

But what legalization has done is greatly reduce overall numbers of arrests, removing the harms of criminalization for many people, including, in absolute numbers, people of color. We havent reached our destination, but we have advanced.

Similar charges are sometimes leveled at LEAD, including by sources quoted in the Filter piece. It is right to note with concern that LEADs exclusion criteria regarding past convictions disproportionately affect people of color because of the systemic racism baked into the criminal justice system and wider society. That must be addressed. But did LEAD ever claim it would end racism in the system? It did not.

Keith Brown, a former LEAD project director, told Filter, All LEAD can do is mitigate or otherwise reduce the harms of racial disparities. It was a criticism of the program, but equally reflects some of what it can achieve: nowhere near everything we want, but still a meaningful difference to many peoples lives.

Neither does the existence of LEAD in any way hold back other forms of progress. I would argue the reverse. The programs role in changing the drug policy conversationincluding within the culture of law enforcement, with all the impact that may have on skeptical members of the publicshould not be underestimated. Examples include calls and support by law enforcement for the decriminalization of simple possession of drugs here and abroad, the need for a safe drug supply, safe consumption sites, drug checking services, the discussion of the failure of drug courts and incarceration, as well as the introduction of social contact referrals, moving people away from the justice system and toward community health and social services.

Helping people now, in whatever ways we can, is just as valid as thinking longer-term.

Many people in the harm reduction community express important concerns about every kind of incremental or imperfect reform. I do, too. It is vital that we air these criticisms, that programs are scrutinized with a view to improvement, and that we never lose sight of our ultimate goals.

But to frame radical and incremental reformsin the context of public opinion and our hard-earned experience of what it takes to winas enemies, rather than different points on the spectrum of positive change, is counterproductive and wrong.

Helping people now, in whatever ways we can, is just as valid as thinking longer-term. By viewing these approaches as complementary and mutually compatible, our movement becomes stronger and does more real-world good.

*The Drug Policy Alliance previously provided a restricted grant to The Influence Foundation, which operates Filter, to support a Drug War Journalism Diversity Fellowship. LEAP was previously the fiscal sponsor of The Influence Foundation.

Image by neo tam from Pixabay

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Our Movement Is Strongest When We Aim HighAnd Work in Current Reality - Filter

This November, Oregon can spark a withdrawal from the War on Drugs – Statesman Journal

Dr. Jeffrey Singer, Guest Opinion Published 4:55 p.m. PT Oct. 16, 2020

Catch up on any news you may have missed. Wochit

If voters approve it, Measure 110 - the Drug Decriminalization and Addiction Treatment Initiative - will reduce possession of all Schedule I through IV controlled substances to Class E violations, punished by a $100 fine.

To qualify as a Class E violation, the amount of a drug an individual may possess cannot be greater than for personal use. Drug dealing or manufacturing would still be punishable.

This is a good start, but Oregonians should look to Portugal for an even better example.

In 2001, Portugal led the European Union in drug overdose deaths. Realizing that treating substance use as a crime was filling jails, fueling corruption, and failing to stop overdose deaths and disease spread, Portugal decriminalized all drugs. Resources for law enforcement were redirected toward harm reduction while drug dealing and manufacturing remain criminal offenses.

In the years since Portugals rate of HIV plunged, drug-related crimes plummeted, and Portugals drug overdose rate is among the lowest in the developed world. Today, a country that decriminalized all drugs nearly 20 years ago reports overdose deaths per million at less than one-thirtieth that of the United States. And while overall drug use by adults mirrors most of the European continent, teen drug use in Portugal has decreased relative to other EU countries.

Election 2020: Oregon's Measure 110 would decriminalize drug possession, expand treatment

Speaking before the Rhode Island General Assembly this past January, Dr. Jaoa Goulao, the architect of Portugals drug policy, explained that the program works because people with substance use disorder are not treated as criminals: If I smoke cigarettes and I get lung cancer, no one puts me in jail. Ill be offered treatment. Ill be treated with dignity even if it comes from my wrong behavior.

He also noted that law enforcement efficiency improved as police were freed fromtasks that were not reducing drug use. Drug users on the street now seek help from officers, who refer them to treatment programs.

Election 2020: Oregon's Measure 110 would decriminalize drug possession, expand treatment

Of course, not every illicit drug user has a substance use disorder. In fact, only 10 to 20%of adults over age 25 who use addictive drugs get hooked. It is perhaps with this insight that Initiative Petition 44 provides the option of a completed health assessment in lieu of a fine. This provides those who want help with an incentive to obtain it. Whats more, it directs expected taxpayer savings resulting from prisons no longer being filled with drug offenders to help fund treatment programs.

There is reason, however, to worry about what kind of programs will be offered.

Another view: Measure 110 would take away addiction treatment and cost lives

Policymakers often overemphasize abstinence-based programs, which have a disappointing track record and dont prioritize treatment with methadone and buprenorphine, which are much more effective.

Dr. Jeffrey Singer(Photo: Courtesy of the CATO Institute)

Oregon has a history of sparking nationwide changes. The Oregon Plan led to the 17th Amendment to the Constitution and the direct election of senators. Voting by mail began in Oregon in 1981. And while a small step in the right direction, this initiative may trigger the end of the destructive War on Drugs.

Jeffrey A. Singer, MD practices general surgery in Phoenix, Arizona, and is a senior fellow at the Cato Institute. He can be reached atjsinger@cato.org

Read or Share this story: https://www.statesmanjournal.com/story/opinion/2020/10/16/decriminalize-possession-small-amounts-drugs-increase-treatment-funding-guest-opinion/3672033001/

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This November, Oregon can spark a withdrawal from the War on Drugs - Statesman Journal

Biden Tries To Gloss Over His Long History of Supporting the Drug War and Draconian Criminal Penalties – Reason

During his ABC "town hall" last night, responding to a question from moderator George Stephanopoulos, Democratic presidential nominee Joe Biden agreed that it was a "mistake" to "support" the Violent Crime Control and Law Enforcement Act of 1994. At the same time, he defended parts of the law, including the Violence Against Women Act, funding to support "community policing" by hiring more officers, and the now-expired federal ban on "assault weapons." He also implied that the real problem was not so much the law itself but the way that states responded to it. "The mistake came in terms of what the states did locally," he said.

Both the question and the answer were highly misleading. First, Biden did not merely "support" the 1994 law; hewrote the damned thing, which he has proudly called "the 1994 Biden Crime Bill." Second, as much as Biden might like to disavow the law's penalty enhancements now that public opinion on criminal justice has shifted, he was proud of them at the time. Third, the 1994 crime bill is just one piece of legislation in Biden's long history of supporting mindlessly punitive responses to drugs and crime.

Biden is trying to gloss over a major theme of his political career. "Every major crime bill since 1976 that's come out of this Congressevery minor crime billhas had the name of the Democratic senator from Delaware, Joe Biden," he bragged in 1993. Now he wants us to believe his agenda was limited to domestic violence, community policing, and gun control.

"Things have changed drastically" since 1994, Biden said last night, noting that "the Black Caucus voted" for the crime bill, and "every black mayor supported it." In other words, now that black politicians and Democrats generally have rejected the idea that criminal penalties can never be too severe, Biden has shifted with the winds of opinion. But as Sen. Cory Booker (DN.J.) noted during a Democratic presidential debate last year, that does not mean we should forget Biden's leading role in the disastrous war on drugs and the draconian criminal justice policies that put more and more people in cages for longer and longer periods of time.

"The crime bill itself did not have mandatory sentences except for two things," Biden said. He mentioned the law's "three strikes and you're out" provision, which required a life sentence for anyone convicted of a violent crime after committing two other felonies, one of which can be a drug offense. He said he "voted against" that provision, which is not exactly true. While he did express concern that the provision was not focused narrowly enough on serious violent crimes, he voted for it as part of the broader bill.

In any case, Biden did not just go along with the crime bill's punitive provisions; he crowed about them. Like a crass car salesman hawking a new model with more of everything, he touted "70 additional enhancements of penalties" and "60 new death penaltiesbrand new60." He denounced as "poppycock" the notion, which would later be defensively deployed by Bill Clinton and Biden himself, that "somehow the Republicans tried to make the crime bill tougher." Biden bragged that he had conferred with "the cops" instead of some namby-pamby "liberal confab" while writing the bill.

As for "what the states did locally," the law was designed to increase incarceration. It provided $10 billion in subsidies for state prison construction, contingent on passage of "truth in sentencing" laws that limited or abolished parole. "What I was against was giving states more money for prison systems," Biden said last night. But that is simply not true. As FactCheck.org noted last year, "Biden did support $6 billion in funding for state prison construction, but not the $10 billion that was part of the final bill."

Despite Biden's implication that he was not a fan of mandatory minimums, he zealously supported them in previous legislation, including the AntiDrug Abuse Acts of 1986 and 1988. The latter law included a five-year mandatory minimum sentence for anyone caught with five grams of crack cocaine, whether or not he was involved in distribution.

As Biden explained it on the Senate floor in 1991 while holding up a quarter, "we said crack cocaine is such a bad deal that if you find someone with this much of ita quarter's worth, not in value, but in sizefive years in jail." To be clear: Biden was not marveling at the blatant injustice of that punishment. He was touting his anti-drug bona fides.

Biden also supported a sentencing policy that treated crack cocaine as if it were 100 times worse than cocaine powder, even though these are simply two different ways of consuming the same drug. Under the 1986 law, possessing five grams of crack with intent to distribute it triggered the same five-year mandatory minimum sentence as 500 grams of cocaine powder; likewise, the 10-year mandatory minimum required five kilograms of cocaine powder but only 50 grams of crack.

Because federal crack offenders were overwhelmingly black, while cocaine powder offenders were more likely to be white or Hispanic, the rule Biden supported meant that darker-skinned defendants received substantially heavier penalties than lighter-skinned defendants for essentially the same offenses. "We may not have gotten it right," Biden conceded 16 years after he helped establish the 100-to-1 rule. Five years later, during an unsuccessful bid for his party's 2008 presidential nomination, he introduced a bill that would have equalized crack and cocaine powder sentences.

The distinction between smoked and snorted cocaine "was a big mistake when it was made," Biden admitted in a speech he gave just before entering the presidential race in 2019, nine years after Congress approved a law that shrank but did not eliminate the sentencing gap. "We thought we were told by the experts that crackwas somehow fundamentally different. It's not different." The misconception, he added, "trapped an entire generation."

These are just a few examples of Biden's enthusiasm for coming down hard on people who dare to defy the government's arbitrary pharmacological decrees. You can read more about that here.

Nowadays, Biden opposes the mandatory minimums and death penalties he championed for decades. But his current position still reflects his commitment to using force against people engaged in peaceful conduct that violates no one's rights.

"I don't believe anybody should be going to jail for drug use," Biden said last night. "They should be going into mandatory rehabilitation. We should be building rehab centers to have these people housed."

While Biden considers that approach enlightened and humane, there is no moral justification for foisting "treatment" on people who do not want it and may not even be addicted. That policy strips people of their liberty, dignity, and moral agency simply because they consume psychoactive substances that politicians do not like. Biden, who in the late 1980s was saying "we have to hold every drug user accountable," now wants to lock drug users in "rehab centers" rather than prisons. If that looks like an improvement, it is only because Biden's prior record is so appalling.

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Biden Tries To Gloss Over His Long History of Supporting the Drug War and Draconian Criminal Penalties - Reason

Mexico’s Ex-Defense Secretary Charged With Helping Cartel Ship Drugs – The Maritime Executive

Gen. Cienfuegos, right, visiting the National Defense University at Fort McNair, Washington, 2013 (U.S. Army)

By The Maritime Executive 10-19-2020 05:28:24

Mexico's former secretary of defense has been arrested in Los Angeles and charged with using his official post to assist the little-known H-2 Cartel with smuggling, including acting as a maritime shipping agent for narcotics.

The defendant, Gen. Salvador Cienfuegos Zepeda, was Mexico's defense secretary from 2012-2018. Federal prosecutors in the Eastern District of New York contend that Cienfuegos accepted bribes from the H-2 Cartel in exchange for a broad range of services, including "locating maritime transportation for drug shipments."

Cienfuegos also allegedly provided high-level cover, ensuring that no Mexican military operations were launched against H-2, using the military to target H-2's rivals, helping H-2 to expand its territory in Sinaloa and giving the cartel inside intelligence about American investigations into its activities. This intelligence included information about a suspected mole, and it "ultimately resulted in the murder of a member of the H-2 Cartel that the [cartel's] senior leadership incorrectly believed was assisting U.S. law enforcement authorities," prosecutors alleged.

H-2 was a remnant of the larger and better-known Beltran-Leyva Organization (BLO). The leader of H-2,Juan Francisco Patron Sanchez, was killed in a Mexican Navyraid involving ahelicopter gunshipin 2017;the cartel is now believed to be substantially defunct, like BLO.

"Due in part to the defendants corrupt assistance, the H-2 Cartelconducted its criminal activity in Mexico without significant interference from the Mexican military and imported thousands of kilograms of cocaine, heroin, methamphetamine, and marijuana into the United States," asserted acting U.S. Attorney for the Eastern District Seth D. DuCharme.

American investigators based their charges against Cienfuegos on a trove of thousands of Blackberry messages that allegedly contain conversations with members of the cartel. The discussions include evidence regarding other high-level Mexican officials that Cienfuegos allegedly put into contact with H-2's leaders.

The arrest raises significant questions about the integrity of Mexico's armed services, which are seen within the country as the least corrupt and most effective national institutions. Given the challenges facing Mexico'scivilian agencies, President Andres Manuel Lopez Obrador has leaned heavily on the military to pursue his agenda, putting military engineers in charge of building a new airport for Mexico City, leaving the army in charge of the long-running war on drugs, and even proposing to transfer control of the nation's major seaports to military leaders.

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Mexico's Ex-Defense Secretary Charged With Helping Cartel Ship Drugs - The Maritime Executive