Category Archives: Artificial Intelligence

Frontex, the European Border and Coast Guard Agency, commissioned RAND Europe to carry out an Artificial intelligence (AI) research study to provide an overview of the main opportunities, challenges and requirements for the adoption of AI-based capabilities in border management.

AI offers several opportunities to the European Border and Coast Guard, including increased efficiency and improving the ability of border security agencies to adapt to a fast-paced geopolitical and security environment. However, various technological and non-technological barriers might influence how AI materializes in the performance of border security functions.

Some of the analyzed technologies included automated border control, object recognition to detect suspicious vehicles or cargo and the use of geospatial data analytics for operational awareness and threat detection.

The findings from the study have now been made public, and Frontex aims to use the data gleaned to shape the future landscape of AI-based capabilities for Integrated Border Management, including AI-related research and innovation projects.

The study identified a wide range of current and potential future uses of AI in relation to five key border security functions, namely: situation awareness and assessment; information management; communication; detection, identification and authentication; and training and exercise.

According to the report, AI is generally believed to bring at least an incremental improvement to the existing ways in which border security functions are conducted. This includes front-end capabilities that end users directly utilize, such as surveillance systems, as well as back-end capabilities that enable border security functions, like automated machine learning.

Potential barriers to AI adoption include knowledge and skills gaps, organizational and cultural issues, and a current lack of conclusive evidence from actual real-life scenarios.

Read the full report at Frontex

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Study Finds Both Opportunities and Challenges for the Use of Artificial Intelligence in Border Management Homeland Security Today - HSToday

Author Stephen Taglieri, KwF Conservation Research Intern

More and more, drones are becoming a normal part of our future. When we firstintroduced the integration of computer science with aerospace engineering to create self aware drones it seemed like an alien concept, but over the last couple of years A.I. has advanced exponentially while drone development has expanded to many conservation studies, says Princess Aliyah Pandolfi, Executive Director of Kashmir World Foundation (KwF).

These flying robots aid workers with daily tasks, and innovation keeps pushing technology in a direction to further help. However, drones dont always have to help people, drones can also be used to safeguard wildlife. Conservationists are chronically underfunded and understaffed, so the use of drones can give much-needed assistance. This is especially true with sea turtle conservation.

Kashmir World Foundation (KwF) was able to introduce Pronatura, Mexicos largestenvironmental conservation group, to the perks of using drones to protect turtles.

Pandolfi created the Fly for Conservation workshop to educate researchers and biologists on the value of custom drones embedded with A.I. on the Edge. In 2018, Marista University of Mrida hosted the Fly for Conservation workshop with KwF staff teaching environmentalists on the Yucatan Peninsula how to build, program, and operate drones to survey sea turtles efficiently.

With aerial visuals, the team released 32 sea turtle hatchlings at a beach in Celestun, Mexico and ended the successful evening with a group picture to document their victory. Sea turtle conservation is usually time-consuming and energy-intensive; drones, and the innovative ways to use them, take pressure off experts so they can spend more time-saving species.

Dr. Melania Lpez-Castro is a conservationist at Pronatura working with sea turtle populations along the Yucatan coastlines. Drones have the potential to help their efforts significantly.

Although the organization is large, Pronatura still struggles with finding enough manpower to survey all the sea turtles that nest on beaches under their jurisdiction. There are many threats to turtles in that area, and Lpez-Castro is working to increase her organizations ability to protect them. They are currently fighting against the effects of plastic pollution, beach erosion, and destructive human activities on the beach.

The Pronatura team walks the beach every morning and evening to find any new turtle nests. Throughout the night, researchers and volunteers work to record the success of hatchlings leaving the nest and avoiding predators on their way to the ocean. Animals such as birds, crabs, coyotes, raccoons, and sharks are all predators of sea turtle hatchlings. If the team runs into an adult turtle while looking for tracks on the beach, they will measure the animal and record whether it has been tagged before. The process is very demanding of the researchers, and it can stress the turtle as well.

This is where KwF steps in to create a more effective and minimally intrusive conservation environment for both humans and turtles. Malini Shivaram, Sean Sewell, and Langston Kosoff are all interns working with the artificial intelligence A.I. program at KwF.

They are developing a new artificial intelligent drone that can help sea turtle conservationists.

Shivaram is an Artificial Intelligence Major at Carnegie Mellon, Sewell an Aeronautical and Astronautical Engineering Major at Stanford, and Kosoff is a Computer Science Major at the University of Chicago. The sea turtle conservation drone embedded with an AI, is called MiSHELL. It is made specifically for detecting sea turtle tracks and identifying the specific species and providing the GPS location of the nest to the biologists.

KwF has partnered with Pronatura to collect data for their research and test MiSHELLs ability to work outside of lab settings. The Convolutional Neural Network (CNN) is trained to process data in real time on a small computer onboard the drone. This type of program is best for image recognition and processing. Once the computer is connected to the flight controller and GPS on the drone, it will be used to fly over beach areas where turtles may have been. Using downward-facing cameras, MiSHELL will be able to process the video data in real time and identify sea turtle tracks as it flies over them.

Conservation teams in the area will then know where the tracks are located and can navigate to them via GPS coordinates. The project will save time finding the animals and give the Pronatura more time to record data and protect the sea turtles.

In an interview, Shivaram said that using AI to identify wildlife in captured images orvideos could be an efficient and fairly accurate way to process data. With MiSHELL, real-time data is sent to on-the-ground conservation teams while the drone captures imagery. The data can be sent to these teams at any time, even if theyre miles down the beach from the drones location. The team will know specifically where to find the tracks, so they wont have to walk meticulously down the beach looking for turtles. MiSHELL can even identify which species of turtle created the tracks on the beach. This is done by analyzing the pattern of these tracks; all seven species of turtle leave different patterned tracks due to the size and shape of their flippers.

Creating a working, reliable AI model takes a great deal of data. KwFs partnership with Pronatura has brought the AI-building process a long way. Researchers in Mexico have recorded videos from the drones flying over turtle tracks and sent them to KwF. These pre-recorded videos are used to train the CNN. The data is fed into the AI so it can learn how to successfully identify the tracks. Shivaram and the rest of the team all work to point out the tracks to the AI initially, and then challenge the program to find the tracks by itself. This process is called training a convolutional neural network, and it allows the AI to identify these patterns in the sand on its own.

This careful process takes a long time. It gives the team time to work out any other bugs, though, before fully putting their trust into the drone. For example, Lpez-Castro observed that the sound of drones spook turtles in the area. While the AI continues to be developed, the drone team at KwF is using research from another project, Eagle Ray, to fix this issue. Eagle Ray is a different kind of drone program which employs silent thrusters, and that technology could cross over to benefit the sea turtle project (Read more about Eagle Ray).

As drone technology gets more advanced, Lpez-Castro and her team cant help but look into the future.

The applications of drone technology in the conservation field are seemingly endless. The Pronatura team believes that the technology could be used for more applications as they get more involved with the drones. One potential use would be to track sea turtle surface movement in clear waters off the coast. Tracking turtles using aerial drones would allow researchers to get close to turtles without noisy boats. This method could also give more information about where sea turtle breeding and feeding grounds are. Drones made for underwater environments could replace metal or satellite tagging programs while being more cost-effective.

This is a huge step in the right direction. When speaking about current tracking methods, Lpez-Castro explains that the most effective way of tagging a turtle in the wild is to clip a metal marker to the animals flipper. Each marker has a unique number that identifies that specific turtle, and researchers can record where and when they see that animal again. The downside is this method does not give any data about the animal between its interactions with researchers, which means that not much is known about turtles when theyre off the beach.

Satellite tags can be used, but they are very expensive and have a high chance of falling off the turtle before enough data is taken. Experts still dont know where sea turtle feeding grounds are, where mating sites are, or the impacts which the disappearance of sea turtles is having on the environment.

Working with what they are given, conservation organizations have been able to make the best of their resources. KwF, and partners, work to give environmentalists the technology and training to grow eco-friendly efforts around the world. The use of artificial intelligence, drones, and other state-of-the-art innovations can change the game when it comes to saving species. Having helped create a MiSHELL, Shivaram affirms that like in many other fields, technology (especially the use of AI) will become prominent in conservation.

Visit our website at http://www.kashmirworldfoundation.org

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Smart Safe Keeping: Blending Artificial Intelligence with Sea Turtle Conservation Steve Taglieri KwF Conservation Research Intern - sUAS News

Henry Ford did not invent the automobile. That was Karl Benz.

But Ford did perfect the assembly line for auto production. That innovation directly led to cars becoming markedly cheaper, putting them within reach of millions of Americans.

In effect, Ford democratized the automobile, and I see a direct analogy to what the University of Florida is doing for artificial intelligence AI, for short.

In July, the University of Florida announced a $100 million public-private partnership with NVIDIA the maker of graphics processing units used in computers that will catapult UFs research strength to address some of the worlds most formidable challenges, create unprecedented access to AI training and tools for under-represented communities and build momentum for transforming the future of the workforce.

At the heart of this effort is HiPerGator AI the most powerful AI supercomputer in higher education. The supercomputer, as well as related tools, training and other resources, is made possible by a donation from UF alumnus Chris Malachowsky as well as from NVIDIA, the Silicon Valley-based technology company he co-founded and a world leader in AI and accelerated computing. State support also plays a critical role, particularly as UF looks to add 100 AI-focused faculty members to the 500 new faculty recently added across the university many of whom will weave AI into their teaching and research.

UF will likely be the nations first comprehensive research institution to integrate AI across the curriculum and make it a ubiquitous part of its academic enterprise. It will offer certificates and degree programs in AI and data science, with curriculum modules for specific technical and industry-focused domains. The result? Thousands of students per year will graduate with AI skills, growing the AI-trained workforce in Florida and serving as a national model for institutions across the country. Ultimately, UFs effort will help to address the important national problem of how to train the nations 21st-century workforce at scale.

Further, due to the unparalleled capabilities of our new machine, researchers will now have the tools to solve applied problems previously out of reach. Already, researchers are eyeing how to identify at-risk students even if they are learning remotely, how to bend the medical cost curve to a sustainable level, and how to solve the problems facing Floridas coastal communities and fresh water supply.

Additionally, UF recently announced it would make its supercomputer available to the entire State University System for educational and research purposes, further bolstering research and workforce training opportunities and positioning Florida to be a national leader in a field revolutionizing the way we all work and live. Soon, we plan to offer access to the machine even more broadly, boosting the national competitiveness of the United States by partnering with educational institutions and private industry around the country.

Innovation, access, economic impact, world-changing technological advancement UFs AI initiative provides all these things and more.

If Henry Ford were alive today, I believe he would recognize the importance of whats happening at UF. And while he did not graduate from college, I believe he would be proud to see it happening at an American public university.

Joe Glover is provost and senior vice president of academic affairs at the University of Florida.

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Heres why UF is going to use artificial intelligence across its entire curriculum | Column - Tampa Bay Times

VERONA, Italy--(BUSINESS WIRE)--Trueblue, after having announced the integration of its Artificial Intelligence Relationship Management with Microsoft Dynamics 365 and Power Platform, officially launches on the market:

AiDEA

Smart Customer Engagement

AiDEA is the new AI driven Omnichannel Customer Engagement suite. The foundation of the solution, represented by Artificial Intelligence , integrates and powers the operational and analytical functionalities based on Microsoft Dynamics 365 and Power Platform, for a holistic and integrated experience, with the goal of revolutionizing the working model of Pharma & Life Science markets, simplifying omni-channel engagement through intuitive and conversational interaction.

Two fundamental components guide the change, whose union had not yet materialized in the reference market: the concrete integration of Big Data in the perspective of Multichannel Management and the use of Artificial Intelligence functionalities and algorithms. The latter is an element that can no longer be postponed from an IT point of view, as it is necessary to drive Customer Engagement processes to satisfy company objectives from both a strategic and an operational point of view.

These elements require a structural change in the approach of organizations and tools, as a generic Customer Relationship Management system is no longer sufficient. It is in fact necessary to adopt specific Smart Omnichannel Customer Engagement solutions, fully enabled in terms of Artificial Intelligence, to have, in a quick, simple and intuitive way, precise indications about one's own customers.

As part of this transition in fact, Pharma companies such as Angelini Pharma, Alfasigma and others are taking this direction with strength and determination with the aim of innovating and achieving their business results faster.

"Artificial Intelligence represents a tremendous opportunity to increase our effectiveness and we want to provide this competitive advantage to our employees thanks to AiDEA" said Pierluigi Antonelli, CEO of Angelini Pharma "After a long and thorough analysis, we identified Trueblue and Microsoft as the best partners to advance our Customer Engagement capabilities by delivering an innovative digital CRM solution that transforms strategy into action.

Trueblue, which has always been at the center of technological and digital innovation for the pharmaceutical industry, thanks to the integration with Microsoft introduces with AiDEA a new paradigm in which Artificial Intelligence is the backbone and key factor of the evolutionary process.

"Through this integration, Trueblue will help companies in the industry accelerate their growth and find new ways to drive Digital Innovation through a wide range of solutions that will enable them to simplify the use of AI in their daily activities," said Marco Bonesini CEO of Trueblue

In todays reality of accelerated digital transformation processes, pharma & life science companies rely on proactive solutions such as AIDEA, integrated with Dynamics 365 and Power Platform, to enable effective omnichannel strategies said Elena Bonfiglioli, Managing Director, HealthCare and Life Sciences, EMEA Regional Lead.

Discover more About Trueblue

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Trueblue Designs the Future of Artificial Intelligence and Analytics for Healthcare With Aidea Integrated With Microsoft Dynamics 365 - Business Wire

CUYAMA, CA - APRIL 28: Overhead irrigation of this newly planted crop of carrots is putting ... [+] pressure on the available groundwater supplies as viewed on April 28, 2020, in Cuyama, California. Located in the northeastern corner of Santa Barbara County, the sparsely populated and extremely arid Cuyama Valley has become an important agricultural region, producing such diverse crops as carrots, pistachios, lettuce, and wine grapes. (Photo by George Rose/Getty Images)

The globalprecision farming marketincludes technology like robotics, imagery, sensors, artificial intelligence (AI), big data and bio-engineering is expected to reach more than $16 billion by 2028, according to aMarch 2021 reportfrom Grand View Research.

What if you could combine AI and traditional aerial imagery to build data sets that help farmers and food processors gain insight into crop heartiness while it was still growing in the field?

Saul Alarcon, an Agronomist atThe Morningstar Companythat sources and processes tomatoes for several tomato-based products, says that new agriculture technologies based on AI can improve farming decisions. "Accuracy and consistency of data are very important to minimize the impact of crop's yield-limiting factors," said Alarcon.

"Smart farming technologies are becoming, in a short period of time, a key alternative in our worldwide efforts to improve the quantity, quality and nutritional value of food," said Alarcon. "Similarly, we firmly believe that it offers great opportunities to improve our environment while helping farmers to remain profitable."

John Bourne, VP of Marketing at Ceres Imaging, says that because food processors are increasingly using AI-powered aerial imagery to help manage their operations, they can now apply that to yield forecasting, quality control and risk mitigation.

"Typically processors pay for imagery and then offer the imagery service as a benefit to growers in their networks at no cost or for subsidized pricing," said Bourne. "This benefits the growers because they get reduced price imagery and product quality control vetted by their processors."

Images paint a picture, but AI images can help provide actionable data for farmers.

"Convolutional neural networks are used to enhance the accuracy of indexes such as segmenting images to identify pixels that belong to the crops we're measuring, and excluding all soil, grass and shadow," said Bourne. "AI can also classify individual plants and the pixels that belong to those plants."

But Bourne says that convolutional neural networks are also used to go from an index to a recommendation for a farmer, which means they could better identify certain acute irrigation issues, such as malfunctioning sprinklers with pins dropped in the imagery and ranking in terms of severity and risk to yield.

Patrick Tokar, Viticulturist atRombauer Vineyardsin Napa Valley, says that the vineyard initially looked into aerial imagery because they were searching for another tool to determine their irrigation needs. The company used Normalized Difference Vegetation Index (NDVI) to help determine the density of a green area in a patch of land but ended up at Ceres Imaging to address irrigation.

"This technology enables us to view the relative water stress for an entire vineyard block as opposed to specific data points within a block," said Tokar.

"What we did not realize when we first started using the service is the amount of correlation between water stress areas and wine quality," said Tokar. "We have traditionally used only NDVI images to map out harvest zones, but given our experiences over the past few years, we now look at the water stress maps in conjunction with the NDVI's."

The aerial data that Ceres processes is transformed into indexes that tell a different crop or yield story based on that index, such as water stress.

"Instead of looking at specific data points in the field to make decisions, aerial imagery gives us literally a bird's eye view of the entire vineyard block," said Tokar. "This enables us to hone in on any problem areas we may not have been aware of otherwise.

Tokar says that by looking at the imagery data, they saved time by planning out specific areas they needed to look at before a site visit, rather than scouting an entire vineyard to find potential problems.

Bourne adds that the primary driver for achieving a high solid percentage optimizes the farmer's irrigation strategy.

"Our most popular index is our water stress index which measures crop transpiration or how much a crop sweats," said Bourne. "The farmer can use the information from the index in several ways such as identifying irrigation issues like clogs and leaks in irrigation equipment."

Bourne says that when they publish the water stress index, the data is passed through an algorithm using convolutional neural networks to look for stress patterns. "The system can then identify issues and predict with a high degree of confidence the cause and severity of such issue such as identifying a grower has an irrigation pressure issue, that impacts six acres with high severity impact on yield," added Bourne.

Bourne adds that farmers can make adjustments caused by human error - an irrigation valve left on, equipment malfunctions, blocked irrigation nozzle, and even optimizing the irrigation schedule.

"For example, aerial imagery could show that the farmer has underwatered or overwatered a parcel of land, or it could show that one section of a block needs more water and one needs less water. So from this, the imager can make zone maps to facilitate watering that fits these issues," said Bourne.

Alarcon says that aerial imagery provided them with high-resolution images of the row and permanent crops. "This technology gives us the advantage of a wider spatial detection of potential yield-limiting factors in crops," says Alarcon.

"Yield uniformity can be improved by assessing low vigor areas during critical crop production stages. Factors like non-sufficient water levels due to low water pressure, plugged-up emitters, insects and disease damage, etc., can be rapidly detected and corrected through the use of crop aerial images.

Bourne believes that this knowledge lets the farmer "dial in" what they want as a result.

"By example, in tomatoes, a common metric is solid as a percentage of total tomato weight its water as a percentage of total tomato," said Bourne. "The grower gets paid more for high-quality tomatoes, so a high solid content tomato generally tastes better can be used fresh for things like salsa which is a higher value and a higher margin use."

Ceres Imaging is based in Oakland, California, and hasraised $35Mto date from institutional investors, including Insight Partners and Romulus Capital.

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This Winery And Tomato Processor Used Artificial Intelligence To Make Their Crops Better - Forbes

The year 2020 may have been one of turmoil and uncertainty across the globe, but artificial intelligence remained on a steady course of growth and further exploration -- perhaps because of the Covid-19 crisis. Healthcare was a big area for AI investment, and concerns about diversity and ethics grew -- but little action has been taken. Most surprisingly of all, while AI job growth accelerated across the world, it flattened in the US.

These are among the key metrics of AI tracked in the latest release of theAI Index, an annual data update from Stanford University'sHuman-Centered Artificial Intelligence Institute. The index tracks AI growth across a range of metrics, from degree programs to industry adoption.

Here are some key measures extracted from the 222-page index:

AI investments rising: The report cites a McKinsey survey that shows the Covid-19 crisis had no effect on their investment in AI, while 27% actually reported increasing their investment. Less than a fourth of businesses decreased their investment in AI.

AI jobs grow worldwide, flatten in the US:Another key metric is the amount of AI-related jobs opening up. Surprisingly, the US recorded a decrease in its share of AI job postings from 2019 to 2020-the first drop in six years. The total number of AI jobs posted in the US also decreased by 8.2% from 2019 to 2020, from 325,724 in 2019 to 300,999 jobs in 2020. This may be attributable to the mature market in the US, the report's authors surmise. Globally, however, demand for AI skills is on the rise, and has grown significantly in the last seven years. On average, the share of AI job postings among all job postings in 2020 is more than five times larger than in 2013. In 2020, industries focused on information (2.8%); professional, scientific, and technical services (2.5%); and agriculture, forestry, fishing, and hunting (2.1%) had the highest share of AI job postings among all job postings in the US.

AI investment in healthcare increased significantly:The product category of "drugs, cancer, molecular, drug discovery" received the greatest amount of private AI investment in 2020, with more than $13.8 billion, 4.5 times higher than 2019, the report states. "The landscape of the healthcare and biology industries has evolved substantially with the adoption of machine learning," the report's authors state. "DeepMind's AlphaFold applied deep learning technique to make a significant breakthrough in the decades-long biology challenge of protein folding. Scientists use ML models to learn representations of chemical molecules for more effective chemical synthesis planning. PostEra, an AI startup used ML-based techniques to accelerate COVID-related drug discovery during the pandemic."

Generative everything:"AI systems can now compose text, audio, and images to a sufficiently high standard that humans have a hard time telling the difference between synthetic and non-synthetic outputs for some constrained applications of the technology. That promises to generate a tremendous range of downstream applications of AI for both socially useful and less-useful purposes."

AI has a diversity and ethics challenge: In 2019, 45% new U.S. resident AI PhD graduates were white -- by comparison, 2.4% were African American and 3.2% were Hispanic, the report states. Plus, "despite growing calls to address ethical concerns associated with using AI, efforts to address these concerns in the industry are limited. For example, issues such as equity and fairness in AI continue to receive comparatively little attention from companies. Moreover, fewer companies in 2020 view personal or individual privacy risks as relevant, compared with in 2019, and there was no change in the percentage of respondents whose companies are taking steps to mitigate these particular risks."

Computer vision has become industrialized:"Companies are investing increasingly large amounts of computational resources to train computer vision systems at a faster rate than ever before. Meanwhile, technologies for use in deployed systems-like object-detection frameworks for analysis of still frames from videos-are maturing rapidly, indicating further AI deployment."

AI conference attendance up, virtually:An important metric of AI adoption is conference attendance. "That's way up. If anything, Covid-19 may have led to a higher number of people participating in AI research conferences, as the pandemic forced conferences to shift to virtual formats, which in turn led to significant spikes in attendance," the survey's authors contend.

More and more information and research is available: The number of AI journal publications grew by 34.5% from 2019 to 2020 -- a much higher percentage growth than from 2018 to 2019 (19.6%), the report's authors state. "In just the last six years, the number of AI-related publications on arXiv grew by more than six-fold, from 5,478 in 2015 to 34,736 in 2020. AI publications represented 3.8% of all peer-reviewed scientific publications worldwide in 2019, up from 1.3% in 2011."

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Artificial intelligence kept expanding through a turbulent year, with some exceptions - ZDNet

On March 1, 2021, the National Security Commission on Artificial Intelligence (NSCAI) released its 700-page Final Report (the Report), which presents NSCAIs recommendations for winning the AI era (The Report can be accessed here). This Report issues an urgent warning to President Biden and Congress: if the United States fails to significantly accelerate its understanding and use of AI technology, it will face unprecedented threats to its national security and economic stability. Specifically, the Report cautions that the United States is not organizing or investing to win the technology competition against a committed competitor, nor is it prepared to defend against AI-enabled threats and rapidly adopt AI applications for national security purposes.

In the Final Report, NSCAI makes a number of detailed policy recommendations to advance the development of AI, machine learning, and associated technologies to comprehensively address the national security and defense needs of the United States. The Report, its findings and recommendations all signal deep concern that the U.S. has underinvested in AI and must play catch-up in order to safeguard its future.

The Commission was established in 2019 as part of the Defense Authorization Act and is chaired by Eric Schmidt, the former CEO of Google, and vice-chaired by former Deputy Secretary of Defense, Robert Work. NSCAI is comprised of 15 commissioners who are technologists, business executives, academic leaders and national security professionals. Twelve of the commissioners were nominated by Congress, and three were nominated by either the Secretary of Defense or Secretary of Commerce.

The Report calls for expansive action by the U.S.to combat the critical national security threats posed by the growth of AI-capable adversaries. Notably, it highlights the perceived vulnerability of the United States economic and military power which, according to the Report, is threatened by its failure to adequately develop a comprehensive strategy to compete in the era of AI-accelerated competition and conflict. Specifically, as described in the Report:

[T]he United States must act now to field AI systems and invest substantially more resources in AI innovation to protect its security, promote its prosperity, and safeguard the future of democracy. Today, the government is not organizing or investing to win the technology competition against a committed competitor, nor is it prepared to defend against AI-enabled threats and rapidly adopt AI applications for national security purposes.

NSCAI calls for wide-sweeping policy changes deemed to be necessary for protecting national security. These recommendations include:

The NSCAI Report has thrown a spotlight on the urgent need for United States policy to address the future significance of AI in the realm of national security as well as U.S. economic interests. We will look to see how the Biden Administration and Congress use these recommendations to shape national policies going forward.

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The U.S. in the AI Era: the National Security Commission on Artificial Intelligence Releases Report Detailing Policy Recommendations - JD Supra

Facebook isn't just a place for individuals to document their lives, or keep up with others. It's actively shaping our lives, politics, and society in ways some consider manipulative. Of particular concern is how Facebook uses artificial intelligence - or A.I. for short - and how the technology may be helping spread misinformation online. Reporter Karen Hao has a new article about this at MIT Technology Review, "How Facebook Got Addicted to Spreading Misinformation." Hao discussed her reporting with GBH All Things Considered host Arun Rath. This transcript has been edited for clarity.

Arun Rath: I think when people think about A.I. on Facebook, they're thinking about targeted ads. Tell us about Facebook's use of A.I., because it's a lot more than that.

Karen Hao: Facebook has thousands of A.I. algorithms running at any one time, and some of them are precisely what you say. But that same technology that figures out what you're interested in is also then recommending to you groups you might like, pages you might like, and filtering the content that you see in your news feed. And the goal for all of these algorithms is ultimately to get users to engage as much as possible - to like, to share, to join these groups or to click into these ads.

Rath: This can, in some contexts, contribute to or instigate violence and even genocide, right?

Hao: Yes. So one thing I discovered through my reporting is that, in 2016, a Facebook researcher named Monica Lee started studying whether the company's algorithms were inadvertantly contributing to extremism or polarization. She found that their recommendation algorithms were linking up users with extremist groups, and that over 60 percent of the users who joined those extremist groups did so because it was recommended by Facebook. Mark Zuckerberg has publicly admitted that the closer certain content comes to violating their standards, the more that users want to engage with it. And because all of these algorithms are trying to maximize your engagement, it inevitably starts to maximize all of this misinformation and hate speech. In very sensitive political environments, this can really exacerbate political and social tensions. This is exactly what happened in Myanmar, where the Buddhist majority saw misinformation about the country's Muslim minority on Facebook, and it ultimately escalated into a genocide.

Rath: This word is kind of a golden word at Facebook - "engagement." Why is that so crucial to this, and what does that term really mean for Facebook?

Hao: I don't quite get into that in my piece, but many other journalists and writers who have, talk about Mark Zuckerberg's obsession with growth. When he started the company, his goal was to get every single person in this world on Facebook. Continuing to grow really hinges on the ability to get users to engage and get them hooked on it. Facebook has kind of supercharged that with all of these algorithms figuring out exactly what you like, what's going to hook you in, and what will keep you there.

Rath: With the ability to measure engagement with this degree of precision, could Facebook adjust it, turn it off, or tone it down?

Hao: That's really the critique of the company now that I've done this reporting. It's not that Facebook doesn't do anything to solve its misinformation problem. It actually has a really big team, called the integrity team, focused on catching misinformation. But that only addresses the symptom. The root problem is that maximizing engagement rewards inflammatory content, and that content is more likely to be polarizing, more likely to be hateful, more likely to be fake. So they're rewarding this content, and then scrambling to catch it after the fact.

Rath: You had this remarkable interview with Facebook's head of A.I., Joaquin Quionero Candela, where you point out not too long after the January 6th insurrection, that we kind of knew there were extremists groups that were going to rally on the Capitol. What did he think about that?

Hao: Joaquin Quionero Candela is the main character in the story. The reason why I wanted to tell the story through his eyes is because he first got Facebook hooked on using A.I. He then switched to leading Facebook's 'responsible' A.I. team. So I asked him, what is Facebook's role in the Capitol riots? What was really hard about reporting this story is that a lot of the responses Joaquin gave me were not necessarily his responses.

Rath: As you're doing this interview, there's a company handler alongside?

Hao: Exactly. So when I asked him what role Facebook had in the Capitol riots, he said he didn't know. When I asked if he thought he should start working on these problems, he said, 'well, I think that's the work of other teams but maybe it's something we'll think about in the future...' Then he said this isn't an A.I. problem, it's just a human nature problem, that people like saying fake things and violent things and hateful things. So I asked him whether he truly believed if the issues with Facebook haven't been made worse by A.I.? And he said, 'I don't know.' That was the end of the interview. To this day, I can't really say whether it was the company line or him that was talking that day.

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New Concerns That Artificial Intelligence Spreads Misinformation On Facebook - wgbh.org

New York, March 19, 2021 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Artificial Intelligence in Military Market by Offering, Technology, Application, Installation Type, Platform, Region - Global Forecast to 2025" - https://www.reportlinker.com/p05366680/?utm_source=GNW

The Artificial Intelligence in Military market includes major players such as BAE Systems Plc. (UK), Northrop Grumman Corporation (US), Raytheon Technologies Corporation (US), Lockheed Martin Corporation (US), Thales Group (US), L3Harris Technologies, Inc. (US), Rafael Advanced defense Systems (Israel), and IBM (US), among others. These players have spread their business across various countries includes North America, Europe, Asia Pacific, Middle East & Africa, and Latin America. COVID-19 has not affected the Ai in military market growth to some extent, and this varies from country to country. Industry experts believe that the pandemic has not affected the demand for Artificial Intelligence in military market in defense applications.

Based on platform, the space segment of the Artificial Intelligence in military market is projected to grow at the highest CAGR during the forecast periodBased on platform, the space segment of the Artificial Intelligence in military market is projected to grow at the highest CAGR during the forecast period.The space AI segment comprises CubeSat and satellites.

Artificial intelligence systems for space platforms include various satellite subsystems that form the backbone of different communication systems. The integration of AI with space platforms facilitates effective communication between spacecraft and ground stations.

Software segment of the Artificial Intelligence in Military market by offering is projected to witness the highest CAGR during the forecast periodBased on offering, the Software segment is projected to witness the highest CAGR during the forecast period.Technological advances in the field of AI have resulted in the development of advanced AI software and related software development kits.

AI software incorporated in computer systems is responsible for carrying out complex operations.It synthesizes the data received from hardware systems and processes it in an AI system to generate an intelligent response.

Software segment is projected to witness the highest CAGR owing to the significance of AI software in strengthening the IT framework to prevent incidents of a security breach.

The North America market is projected to contribute the largest share from 2020 to 2025 in the Artificial Intelligence in Military marketThe US and Canada are key countries considered for market analysis in the North American region.This region is expected to lead the market from 2020 to 2025, owing to increased investments in AI technologies by countries in this region.

This market is led by the US, which is increasingly investing in AI systems to maintain its combat superiority and overcome the risk of potential threats on computer networks. The US plans to increase its spending on AI in military to gain a competitive edge over other countries.The North America US is recognized as one of the key manufacturers, exporters, and users of AI systems worldwide and is known to have the strongest AI capabilities. Key manufacturers of Ai systems in the US include Lockheed Martin, Northrop Grumman, L3Harris Technologies, Inc., and Raytheon. The new defense strategy of the US indicates an increase in Ai spending to include advanced capabilities in existing defense systems of the US Army to counter incoming threats.

The break-up of the profile of primary participants in the Artificial Intelligence in Military market: By Company Type: Tier 1 35%, Tier 2 45%, and Tier 3 20% By Designation: C Level 35%, Director Level 25%, and Others 40% By Region: North America 25%, Europe 15%, Asia Pacific 45%, Middle East 10%, RoW 5%

Major companies profiled in the report include BAE Systems Plc. (UK), Northrop Grumman Corporation (US), Raytheon Technologies Corporation (US), Lockheed Martin Corporation (US), Thales Group (US), L3Harris Technologies, Inc. (US), Rafael Advanced Defense Systems Ltd. (Israel), and IBM (US). (29 Companies)

Research Coverage:This research report categorizes the Ai in Military market basis of Application (Information Processing, Warfare Platforms, Threat Monitoring, Planning & Allocation, Cybersecurity, Simulation & Training, Logistics & Transportation, Surveillance & Situational Awareness, Battlefield Healthcare, Others), Platform (Airborne, Naval, Land, Space), Technology (Machine Learning, Natural Language Processing, Context-Aware Computing, Computer Vision, Intelligent Virtual Agent, Others), Installation Type (New Installation, Upgradation), Offering (Hardware, Software, Services), and major Regions, namely, North America, Europe, Asia Pacific, Middle East& Africa, and Latin America.The scope of the report covers detailed information regarding the major factors, such as drivers, challenges, and opportunities, influencing the growth of the AI in military market.

A detailed analysis of the key industry players has been done to provide insights into their business overviews; solutions and services; key strategies; new product launches, contracts, partnerships, collaborations, expansions, acquisitions, and new product development associated with the Artificial Intelligence in Military market.

Reasons to buy this report:The report will help the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall Artificial Intelligence in Military market and the subsegments.This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and to plan suitable go-to-market strategies.

The report also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.

The report provides insights on the following pointers: Market Penetration: Comprehensive information on Ai in Military products/ solutions offered by the top players in the market Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product launches in the Artificial Intelligence in Military market Market Development: Comprehensive information about lucrative markets the report analyses the Artificial Intelligence in Military market across varied regions Market Diversification: Exhaustive information about new products, untapped geographies, recent developments, and investments in the AI in Military market Competitive Assessment: In-depth assessment of market shares, growth strategies, products, and manufacturing capabilities of leading players in the Artificial Intelligence in Military marketRead the full report: https://www.reportlinker.com/p05366680/?utm_source=GNW

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The Artificial Intelligence in military market is estimated at USD 6.3 billion in 2020 and is projected to reach USD 11.6 billion by 2025, at a CAGR...

DUBLIN, March 19, 2021 /PRNewswire/ -- The "Artificial Intelligence in Supply Chain Management Market by Technology, Processes, Solutions, Management Function (Automation, Planning and Logistics, Inventory, Risk), Deployment Model, Business Type and Industry Verticals 2021 - 2026" report has been added to ResearchAndMarkets.com's offering.

This report provides detailed analysis and forecasts for AI in SCM by solution (Platforms, Software, and AI as a Service), solution components (Hardware, Software, Services), management function (Automation, Planning and Logistics, Inventory Management, Fleet Management, Freight Brokerage, Risk Management, and Dispute Resolution), AI technologies (Cognitive Computing, Computer Vision, Context-aware Computing, Natural Language Processing, and Machine Learning), and industry verticals (Aerospace, Automotive, Consumer Goods, Healthcare, Manufacturing, and others).

This is the broadest and detailed report of its type, providing analysis across a wide range of go-to-operational process considerations, such as the need for identity management and real-time location tracking, and market deployment considerations, such as AI type, technologies, platforms, connectivity, IoT integration, and deployment model including AI-as-a-Service (AIaaS). Each aspect evaluated includes forecasts from 2021 to 2026 such as AIaaS by revenue in China. It provides an analysis of AI in SCM globally, regionally, and by country including the top ten countries per region by market share.

The report provides an analysis of leading companies and solutions that are leveraging AI in their supply chains and those they manage on behalf of others, with an evaluation of key strengths and weaknesses of these solutions. It assesses AI in SCM by industry vertical and application such as material movement tracking and drug supply management in manufacturing and healthcare respectively. The report also provides a view into the future of AI in SCM including analysis of performance improvements such as optimization of revenues, supply chain satisfaction, and cost reduction.

Select Report Findings:

Modern supply chains represent complex systems of organizations, people, activities, information, and resources involved in moving a product or service from supplier to customer. Supply Chain Management (SCM) solutions are typically manifest in software architecture and systems that facilitate the flow of information among different functions within and between enterprise organizations.

Leading SCM solutions catalyze information sharing across organizational units and geographical locations, enabling decision-makers to have an enterprise-wide view of the information needed in a timely, reliable and consistent fashion. Various forms of Artificial Intelligence (AI) are being integrated into SCM solutions to improve everything from process automation to overall decision-making. This includes greater data visibility (static and real-time data) as well as related management information system effectiveness.

In addition to fully automated decision-making, AI systems are also leveraging various forms of cognitive computing to optimize the combined efforts of artificial and human intelligence. For example, AI in SCM is enabling improved supply chain automation through the use of virtual assistants, which are used both internally (within a given enterprise) as well as between supply chain members (e.g. customer-supplier chains). It is anticipated that virtual assistants in SCM will leverage an industry-specific knowledge database as well as company, department, and production-specific learning.

AI-enabled improvements in supply chain member satisfaction causes a positive feedback loop, leading to better overall SCM performance. One of the primary goals is to leverage AI to make supply chain improvements from production to consumption within product-related industries as well as create opportunities for supporting "servitization" of products in a cloud-based "as a service" model. AI will identify opportunities for supply chain members to have greater ownership of "outcomes as a service" and control of overall product/service experience and profitability.

With Internet of Things (IoT) technologies and solutions taking an ever-increasing role in SCM, the inclusion of AI algorithms and software-driven processes with IoT represents a very important opportunity to leverage the Artificial Intelligence of Things (AIoT) in supply chains. More specifically, AIoT solutions leverage the connectivity and communications power of IoT, along with the machine learning and decision-making capabilities of AI, as a means of optimizing SCM by way of data-driven managed services.

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction2.1 Supply Chain Management2.1.1 Challenges2.1.2 Opportunities2.2 AI in SCM2.2.1 Key AI Technologies for SCM2.2.2 AI and Technology Integration

3.0 AI in SCM Challenges and Opportunities3.1 Market Dynamics3.1.1 Companies with Complex Supply Chains3.1.2 Logistics Management Companies3.1.3 SCM Software Solution Companies3.2 Technology and Solution Opportunities3.2.1 Leverage Artificial Intelligence (AI)3.2.1.1 Integrate AI with Existing Processes3.2.1.2 Integrate AI with Existing Systems3.2.2 Integrate AI with Internet of Things (IoT)3.2.2.1 Leverage AIoT Platforms, Software, and Services3.2.2.2 Leverage Data as a Service Providers3.3 Implementation Challenges3.3.1 Management Friction3.3.2 Legacy Processes and Procedures3.3.3 Outsource AI SCM Solution vs. Legacy Integration

4.0 Supply Chain Ecosystem Company Analysis4.1 Vendor Market Share4.2 Top Vendor Recent Developments4.3 3M4.4 Adidas4.5 Amazon4.6 Arvato SCM Solutions4.7 BASF4.8 Basware4.9 BMW4.10 C. H.Robinson4.11 Cainiao Network (Alibaba)4.12 Cisco Systems4.13 ClearMetal4.14 Coca-Cola Co.4.15 Colgate-Palmolive4.16 Coupa Software4.17 Descartes Systems Group4.18 Diageo4.19 E2open4.20 Epicor Software Corporation4.21 FedEx4.22 Fraight AI4.23 H&M4.24 HighJump4.25 Home Depot4.26 HP Inc.4.27 IBM4.28 Inditex4.29 Infor Global Solutions4.30 Intel4.31 JDA4.32 Johnson & Johnson4.33 Kimberly-Clark4.34 L'Oreal4.35 LLamasoft Inc.4.36 Logility4.37 Manhattan Associates4.38 Micron Technology4.39 Microsoft4.40 Nestle4.41 Nike4.42 Novo Nordisk4.43 NVidia4.44 Oracle4.45 PepsiCo4.46 Presenso4.47 Relex Solution4.48 Sage4.49 Samsung Electronics4.50 SAP4.51 Schneider Electric4.52 SCM Solutions Corp.4.53 Splice Machine4.54 Starbucks4.55 Teknowlogi4.56 Unilever4.57 Walmart4.58 Xilinx

5.0 AI in SCM Market Case Studies5.1 IBM Case Study with the Master Lock Company5.2 BASF: Supporting smarter supply chain operations with cognitive cloud technology5.3 Amazon Customer Retention Case Study5.4 BMW Employs AI for Logistics Processes5.5 Intelligent Revenue and Supply Chain Management5.6 AI-Powered Customer Experience5.7 Rolls Royce uses AI to safely transport its Cargo5.8 Robots deliver medicine, groceries and packages with AI5.9 Lineage Logistics Company Case Study

6.0 AI in SCM Market Analysis and Forecasts 2021 - 20266.1 AI in SCM Market 2021 - 20266.2 AI in SCM by Solution 2021 - 20266.2.1 Platforms6.2.2 Software6.2.3 AI as a Service6.3 AI in SCM by Solution Components 2021 - 20266.3.1 Hardware6.3.1.1 Non-IoT Device6.3.1.2 IoT Embedded Device6.3.1.2.1 Security Devices6.3.1.2.2 Surveillance Robots and Drone6.3.1.2.3 Networking Devices6.3.1.2.4 Smart Appliances6.3.1.2.5 Healthcare Device6.3.1.2.6 Smart Grid Devices6.3.1.2.7 In-Vehicle Devices6.3.1.2.8 Energy Management Device6.3.1.3 Components6.3.1.3.1 Wearable and Embedded Components6.3.1.3.1.1 Real-Time Location System (RTLS)6.3.1.3.1.2 Barcode6.3.1.3.1.3 Barcode Scanner6.3.1.3.1.4 Barcode Stickers6.3.1.3.1.5 RFID6.3.1.3.1.6 RFID Tags6.3.1.3.1.7 Sensor6.3.1.3.2 Processors6.3.2 Software6.3.3 Services6.3.3.1 Professional Services6.4 AI in SCM by Management Function 2021 - 20266.4.1 Automation6.4.2 Planning and Logistics6.4.3 Inventory Management6.4.4 Fleet Management6.4.5 Virtual Assistance6.4.6 Freight Brokerage6.4.7 Risk Management and Dispute Resolution6.5 AI in SCM by Technology 2021 - 20266.5.1 Cognitive Computing6.5.2 Computer Vision6.5.3 Context-aware Computing6.5.4 Natural Language Processing6.5.5 Predictive Analytics6.5.6 Machine Learning6.5.6.1 Reinforcement Learning6.5.6.2 Supervised Learning6.5.6.3 Unsupervised Learning6.5.6.4 Deep Learning6.6 AI in SCM by Industry Vertical 2021 - 20266.6.1 Aerospace and Government6.6.2 Automotive and Transportation6.6.3 Retail and Consumer Electronics6.6.4 Consumer Goods6.6.5 Healthcare6.6.6 Manufacturing6.6.7 Building and Construction6.6.8 Others6.7 AI in SCM by Deployment 2021 - 20266.7.1 Cloud Deployment6.8 AI in SCM by AI System 2021 - 20266.9 AI in SCM by AI Type 2021 - 20266.10 AI in SCM by Connectivity6.10.1 Non-Telecom Connectivity6.10.2 Telecom Connectivity6.10.3 Connectivity Standard6.10.4 Enterprise6.11 AI in SCM Market by IoT Edge Network 2021 - 20266.12 AI in SCM Analytics Market 2021 - 20266.13 AI in SCM Market by Intent Based Networking 2021 - 20266.14 AI in SCM Market by Virtualization 2021 - 20266.15 AI in SCM Market by 5G Network 2021 - 20266.16 AI in SCM Market by Blockchain Network 2021 - 20266.17 AI in SCM by Region 2021 - 20266.17.1 North America6.17.2 Asia Pacific6.17.3 Europe6.17.4 Middle East and Africa6.17.5 Latin America6.18 AI in SCM by Country6.18.1 Top Ten Country Market Share6.18.2 USA6.18.3 China6.18.4 Canada6.18.5 Mexico6.18.6 Japan6.18.7 UK6.18.8 Germany6.18.9 South Korea6.18.10 France6.18.11 Russia

7.0 Summary and Recommendations

For more information about this report visit https://www.researchandmarkets.com/r/bxs2vn

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Worldwide Artificial Intelligence in Supply Chain Management Industry to 2026 - Featuring 3M, Adidas and Amazon - PRNewswire