Category Archives: Ai

Artificial intelligence can be a force for good, but society needs to be careful to make sure its negative aspects do not outweigh its positives, Salesforce co-founder Marc Benioff told CNBC's Jim Cramer on Wednesday.

"AI has tremendous opportunity, but technology is never good or bad, it's what we do with the technology that matters," the billionaire entrepreneur and philanthropist said on "Mad Money."

Benioff, co-CEO and chairman of Salesforce, said there could be "dramatic consequences" as AI use in the military accelerates, for example. The Pentagon released its first AI strategy in February.

"But we can use AI for good as well," said Benioff, who is promoting "Trailblazer," the new book he co-authored with Salesforce executive Monica Langley.

Benioff pointed to a drone project that Salesforce is undertaking alongside experts from the University of California, Santa Barbara.

AI experts from Salesforce are working with university researchers to analyze drone footage, in almost real time, to identify great white sharks off the Southern California coast. Shark activity off the California coast has increased in recent years, prompting safety concerns for beachgoers.

"I just showed you how we have a drone running in Santa Barbara with AI, which spotted a great white shark heading for a surf camp," Benioff told Cramer. "And they called the beach, able to get the kids off the beach that's AI for good."

Benioff said he considers AI to be one of the central components of the Fourth Industrial Revolution, which is "characterized by a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres," according to the World Economic Forum.

"There's a lot of AI for good, there's gonna be AI for other things too," Benioff said. "We need to keep our eye on both."

Disclosure: Cramer's charitable trust owns shares of Salesforce.com.

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Marc Benioff: We need to closely watch artificial intelligence to ensure it is a force for good - CNBC

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Artificial Intelligence is poised to disrupt many industries, but education arena has not typically been at the forefront of such conversations. If it has been included at all, the narrative has been in a more abstract manner than actual application. And even though several companies such as Carnegie Learning and Content Technologies, Inc have taken either more adult learning approaches or those that are deeply rooted in tech, the space is still anyones game with new trends to be developed for Gen Z.

The industry is an important one not only for its ability to generate an entirely new level of learning but also because of the very real business opportunity in the space. Indeed, the artificial intelligence in education size is forecasted at a market size worth $6 billion dollars by 2024. Thus, the race is on now among companies that can consistently produce quality content with economical pricing supported by artificial intelligence and machine learning. While there are many startups entering the fray, an established company called UnfoldU has not only created a well-respected brand in India but is also now poised to bring it expertise to North America, the United Kingdom and Australia.

UnfoldU says that it the company is now nearing one million users per year and is currently in it fifth year of operation. The company began a successful business model by targeting middle class families which never had access to the online education but wanted it to supplement education in physical, traditional avenues. So founder Harish Bajaj actually decided to self-fund both the educational platform as well as the solid infrastructure that could support the Internet in India. Although Bajaj began his career in the marketing arena he was able to pivot and focused on hiring top tech developers in the region to convert his vision into reality. To finance the endeavor he borrowed funds from the people who believed in his business idea and invested his entire savings.

Once launched, the result was that the company began to see families use UnfoldU to either replace or supplement their childs educational needs. The business began to build over time because parents could actually track the growth of their childs formal educational in new ways, and they could see results.

UnfoldU is focusing on school education from first to twelfth grade, explains Bajaj. We are not providing any certificate courses or MOOC courses, we are only focused on primary and secondary school education, and we are now providing extension to the school studies by providing content which is backed with artificial intelligence and machine learning.

Student of Unfoldu supported by the power of AI

Bajaj says that young students in India are using this platform to enhance their studies with each users experience being personalized through such emerging tech capabilities. If a student is believed to be acquiring knowledge at a faster pace, the app automatically adjusts itself. If a student is taking time or has issues, the app also automatically adjusts itself and simplifies the course in an appropriate manner.

I started this journey from scratch, and in the early days, accumulating talent for developing quality online education content was very difficult, expensive and elusive. People have now started to realize the potential of the internet based education and in the coming few years it will be massively adopted, explains Bajaj.

Having overcome the hurdles successfully in this market, Bajaj is eager to move to additional territories and drive the brand as both an online school content provide and AI & ML technology company. The company has developed proprietary technology which focuses on the attitude and learning speed of a student. The codes are based on parameters such as learning speed and exam performance. However, Bajaj explains that such questions are neither built nor fetched from a common pool of questions, but instead created on the basis of several internal parameters that are more deeply tied to additional variables around behavior and cognitive responses. The additional competitive advantage in this system is that AI also informs parents about the performance via computer generated voice calls that actually the parents and share the feedback. The parents can then communicate with the AI which is then further integrated into the system.UnfoldUs focus is to eventually cross into the currently controversial realm of having AI can completely replace teachers to ensure a faster and more robust educational method.

Bajaj says that AI in education is beyond creating content. The real secret is using it to actually create smart content which is auto-generated. In the next 20-25 years, we may actually witness human brain directly being programmed, he adds.

In a twist the UnfoldU is also going to launch an IEO to take the company to the next level. The tokens will have real use case scenarios in that they can be used for buying courses. The company has already launched its whitepaper and will hit the markets next month. Tokens of the company are currently trading on BitLux OTC exchange. The additional funds are intended to help the company move deeper into its plans to mesh AR and VR with education to help its customers learn with greater impact and better retain information.

In 1980s faster computers were just a dream, now even the smartphones in our pockets are 50 times more powerful than the supercomputers of that era. Using smart education backed by artificial intelligence will become just as commonplace and advanced. We do not wish to replace teachers completely, but make quality education more accessible by those students who cannot afford it. Nothing can replace the human mind, but supporting it with advanced technology couldnt hurt.

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Heres Whats Next At The Explosive Intersection Of AI And On-Line Education - Forbes

Amid growing concern over the threat of AI-enabled systems to perpetuate discrimination and bias and infringe upon privacy, California has introduced several bills intended to curb negative impacts. Primary among them are bills related to mitigating the negative impacts of specific AI-enabled technologies such as facial recognition systems. On May 14, 2019, San Francisco became the first major US city to ban the use of facial recognition technology by city agencies and law enforcement. Two months later, the neighbouring city of Oakland implemented similar restrictions.

These may be city-level laws, but their passing has influenced state and federal legislation. In California, a bill called the Body Camera Accountability Act seeks to prohibit the use of facial recognition in police body cameras, while another would require businesses to publicly disclose their use of facial recognition technology. At the federal level, four pieces of legislation are currently being proposed to limit the use of this technology, especially in law enforcement.

In the wake of the EUs transformative General Data Protection Regulation, California passed the US first domestic data privacy law. The California Consumer Privacy Act (CCPA) became law in 2018 and is set to go into effect in January 2020. The CCPA gives consumers the right to ask businesses to disclose the data they hold on them, request deletion of data, restrict the sale of their data to third parties, and sue for data breaches. This Act has made its influence felt at the federal level too, prompting the development of a federal data privacy law. These data privacy laws are particularly relevant to data-dependent fields like AI.

In response to the serious threat that AI-enabled bots and deepfakes pose for election integrity, the California government has pushed forward progressive pieces of legislation that have influenced federal and international efforts. Passed in 2018, the Bots Disclosure Act makes it unlawful to use a bot to influence a commercial transaction or a vote in an election without disclosure in California. This includes bots deployed by companies in other states and countries, which requires those companies to either develop bespoke standards for Californian residents or harmonize their strategies across jurisdictions to maintain efficiency. At the federal level, the Bots Disclosure and Accountability Act includes many of the same strategies proposed in California. The California Anti-Deepfakes Bill seeks to mitigate the spread and impact of malicious political deepfakes before an election and the federal Deepfakes Accountability Act seeks to do the same.

While California may be leading the implementation of responsible AI governance strategies, ill-conceived laws, especially those that influence similar strategies at federal and international levels, will cause more harm than good. Take for example the Bots Disclosure Act; some commentators have decried a lack of clarity in the Act around what is and is not determined to be a bot and the roles and responsibilities of parties, especially platforms, to identify and stem the influence of malicious bots. This weakens its implementability and impact. Federal initiatives modeled after Californias law will serve to only further erode accountability and public trust.

There is also the risk that beneficial legislation could become unhelpfully politicized. We are seeing increasing federal pushback against the California effect, as exemplified by recent efforts to revoke Californias ability to implement stricter emission standards than federal guidelines. Federal initiatives may seek to curtail the states impact on national and international standards for responsible AI governance. This is already being witnessed in federal efforts to preempt the CCPA.

California is quickly pushing forward AI legislation, ranging from oversight over discrimination and bias to protecting privacy and election integrity. Californias progressive AI legislation has already had a marked influence on federal efforts, and will likely have global reach if California-based AI companies, including Google, Facebook, and OpenAI, alter their practices. The state has an opportunity and obligation to lead the way in establishing effective standards and oversight that ensures AI systems are developed and deployed in a safe and responsible manner. California can provide guidance on responsible AI governance for the rest of the country and the world, but caution must be taken to implement due diligence in identifying and mitigating any negative impacts before its too late.

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Ethical AI: What can the world learn from California? - World Economic Forum

Artificial intelligence research organization OpenAI has achieved a new milestone in its quest to build general purpose, self-learning robots. The groups robotics division says Dactyl, its humanoid robotic hand first developed last year, has learned to solve a Rubiks cube one-handed. OpenAI sees the feat as a leap forward both for the dexterity of robotic appendages and its own AI software, which allows Dactyl to learn new tasks using virtual simulations before it is presented with a real, physical challenge to overcome.

In a demonstration video showcasing Dactyls new talent, we can see the robotic hand fumble its way toward a complete cube solve with clumsy yet accurate maneuvers. It takes many minutes, but Dactyl is eventually able to solve the puzzle. Its somewhat unsettling to see in action, if only because the movements look noticeably less fluid than human ones and especially disjointed when compared to the blinding speed and raw dexterity on display when a human speedcuber solves the cube in a matter of seconds.

But for OpenAI, Dactyls achievement brings it one step closer to a much sought-after goal for the broader AI and robotics industries: a robot that can learn to perform a variety of real-world tasks, without having to train for months to years of real-world time and without needing to be specifically programmed.

Plenty of robots can solve Rubiks cubes very fast. The important difference between what they did there and what were doing here is that those robots are very purpose-built, says Peter Welinder, a research scientist and robotics lead at OpenAI. Obviously theres no way you can use the same robot or same approach to perform another task. The robotics team at OpenAI have very different ambitions. Were trying to build a general purpose robot. Similar to how humans and how our human hands can do a lot of things, not just a specific task, were trying to build something that is much more general in its scope.

Welinder is referencing a series of robots over the last few years that have pushed Rubiks cube solving far beyond the limitations of human hands and minds. In 2016, semiconductor maker Infineon developed a robot specifically to solve a Rubiks cube at superhuman speeds, and the bot managed to do so in under one second. That crushed the sub-five-second human world record at the time. Two years later, a machine developed by MIT solved a cube in less than 0.4 seconds. In late 2018, a Japanese YouTube channel called Human Controller even developed its own self-solving Rubiks cube using a 3D-printed core attached to programmable servo motors.

In other words, a robot built for one specific task and programmed to perform that task as efficiently as possible can typically best a human, and Rubiks cube solving is something software has long ago mastered. So developing a robot to solve the cube, even a humanoid one, is not all that remarkable on its own, and less so at the sluggish speed Dactyl operates.

But OpenAIs Dactyl robot and the software that powers it are much different in design and purpose than a dedicated cube-solving machine. As Welinder says, OpenAIs ongoing robotics work is not aimed at achieving superior results in narrow tasks, as that only requires you develop a better robot and program it accordingly. That can be done without modern artificial intelligence.

Instead, Dactyl is developed from the ground up as a self-learning robotic hand that approaches new tasks much like a human would. Its trained using software that tries, in a rudimentary way at the moment, to replicate the millions of years of evolution that help us learn to use our hands instinctively as children. That could one day, OpenAI hopes, help humanity develop the kinds of humanoid robots we know only from science fiction, robots that can safely operate in society without endangering us and perform a wide variety of tasks in environments as chaotic as city streets and factory floors.

To learn how to solve a Rubiks cube one-handed, OpenAI did not explicitly program Dactyl to solve the toy; free software on the internet can do that for you. It also chose not to program individual motions for the hand to perform, as it wanted it to discern those movements on its own. Instead, the robotics team gave the hands underlying software the end goal of solving a scrambled cube and used modern AI specifically a brand of incentive-based deep learning called reinforcement learning to help it along the path toward figuring it out on its own. The same approach to training AI agents is how OpenAI developed its world-class Dota 2 bot.

But until recently, its been much easier to train an AI agent to do something virtually playing a computer game, for example than to train it to perform a real-world task. Thats because training software to do something in a virtual world can be sped up, so that the AI can spend the equivalent of tens of thousands of years training in just months of real-world time, thanks to thousands of high-end CPUs and ultra-powerful GPUs working in parallel.

Doing that same level of training performing a physical task with a physical robot isnt feasible. Thats why OpenAI is trying to pioneer new methods of robotic training using simulated environments in place of the real world, something the robotics industry has only barely experimented with. That way, the software can practice extensively at an accelerated pace across many different computers simultaneously, with the hope that it retains that knowledge when it begins controlling a real robot.

Because of the training limitation and obvious safety concerns, robots used commercially today do not utilize AI and instead are programmed with very specific instructions. The way its been approached in the past is that you use very specialized algorithms to solve tasks, where you have an accurate model of both the robot and the environment in which youre operating, Welinder says. For a factory robot, you have very accurate models of those and you know exactly the environment youre working on. You know exactly how it will be picking up the particular part.

This is also why current robots are far less versatile than humans. It requires large amounts of time, effort, and money to reprogram a robot that assembles, say, one specific part of an automobile or a computer component to do something else. Present a robot that hasnt been properly trained with even a simple task that involves any level of human dexterity or visual processing and it would fail miserably. With modern AI techniques, however, robots could be modeled like humans, so that they can use the same intuitive understanding of the world to do everything from opening doors to frying an egg. At least, thats the dream.

Were still decades away from that level of sophistication, and the leaps the AI community has made on the software side like self-driving cars, machine translation, and image recognition has not exactly translated to next-generation robots. Right now, OpenAI is just trying to mimic the complexity of one human body part and to get that robotic analog to operate more naturally.

Thats why Dactyl is a 24-joint robotic hand modeled after a human hand, instead of the claw or pincer style robotic grippers you see in factories. And for the software that powers Dactyl to learn how to utilize all of those joints in a way a human would, OpenAI put it through thousands of years of training in simulation before trying the physical cube solve.

If youre training things on the real world robot, obviously whatever youre learning is working on what you actually want to deploy your algorithm on. In that way, its much simpler. But algorithms today need a lot of data. To train a real world robot, to do anything complex, you need many years of experience, Welinder says. Even for a human, it takes a couple of years, and humans have millions of years of evolution to have the learning capabilities to operate a hand.

In a simulation, however, Welinder says training can be accelerated, just like with game-playing and other tasks popular as AI benchmarks. This takes on the order of thousands of years to train the algorithm. But this only takes a few days because we can parallelize the training. You also dont have to worry about the robots breaking or hurting someone as youre training these algorithms, he adds. Yet researchers have in the past run into considerable trouble trying to get virtual training to work on physical robots. OpenAI says it is among the first organizations to really see progress in this regard.

When it was given a real cube, Dactyl put its training to use and solved it on its own, and it did so under a variety of conditions it had never been explicitly trained for. That includes solving the cube one-handed with a glove on, with two of its fingers taped together, and while OpenAI members continuously interfered with it by poking it with other objects and showering it with bubbles and pieces of confetti-like paper.

We found that in all of those perturbations, the robot was still able to successfully turn the Rubiks cube. But it did not go through that in training, says Matthias Plappert, Welinders fellow OpenAIs robotic team lead. The robustness that we found when we tried this on the physical robot was surprising to us.

Thats why OpenAI sees Dactyls newly acquired skill as equally important for both the advancement of robotic hardware and AI training. Even the most advanced robots in the world right, like the humanoid and dog-like bots developed by industry leader Boston Dynamics, cannot operate autonomously, and they require extensive task-specific programming and frequent human intervention to carry out even basic actions.

OpenAI says Dactyl is a small but vital step toward the kind of robots that might one day perform manual labor or household tasks and even work alongside humans, instead of in closed-off environments, without any explicit programming governing their actions.

In that vision for the future, the ability for robots to learn new tasks and adapt to changing environments will be as much about the flexibility of the AI as it is about the robustness of the physical machine. These methods are really starting to demonstrate that these are the solutions to handling all the inherent complication and the messiness of the physical world we live in, Plappert says.

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OpenAIs AI-powered robot learned how to solve a Rubiks cube one-handed - The Verge

Turkey is about to add a new addition to its high technology investments and projects in order to increase efficiency in preventing smuggling and reducing formalities at customs.

A total of 68 X-Ray Vehicle and Container Scanning systems, which provide detailed analysis of all kinds of vehicles and containers entering and exiting from customs gates without requiring physical intervention, were installed in all customs gates and ports with high trade volume, Trade Minister Ruhsar Pekcan said.

"The Scanning Network Project will be completely developed with our local and national resources and will automatically identify illegal crime and threat elements in vehicle or cargo by using artificial intelligence (AI) technologies," Pekcan told Anadolu Agency (AA) in an interview yesterday.

These high-tech systems provide x-ray film of vehicles and containers and ensure that customs controls, which can last for hours, take place quickly and in a qualified way within minutes, she said.

She stressed that the ministry has established technical infrastructure to carry out customs controls in a manner that does not break the pace of international trade and to combat illegal trade in the most effective way by keeping public health and safety at the forefront.

Indicating that these devices are also an important instrument to combat smuggling and all the elements of illegal crime in national security, the minister continued, "The Trade Ministry is closely following the developments in detection technologies around the world and rapidly adding new equipment to its inventory that will contribute to increasing efficiency in the fight against smuggling."

According to Pekcan, the Scanning Network Project will be completely developed with Turkey's local and national resources by the Scientific and Technological Research Council of Turkey (TBTAK) and will be implemented by the General Directorate of Customs Protection.

By using artificial intelligence and machine learning technologies, the system will automatically identify illegal criminal and threatening elements with certain features in vehicles or cargo. Thus, the operator's margin of error during analysis and manual review times will be reduced to a minimum.

Pekcan noted that X-ray scanning systems will work in an integrated structure under the project and that X-ray images taken at customs gates will be collected in the Command and Control Center of the ministry, and will be dispatched, managed and analyzed from a single center.

Emphasizing that this will be particularly useful in preventing unregistered entry of goods into the country, Pekcan added, "In this way, for instance, X-ray images of a transit vehicle scanned as it enters our country from Habur [customs gate] can be examined by all relevant units and when the vehicle is scanned as it travels abroad from Kapkule [customs gate], the images can be compared. In this way, attempts to leave the transit goods inside our country without paying taxes will be determined and tax loss will be prevented to a great extent."

The minister further stated that the data and information generated in the X-ray systems will be shared with customs authorities of neighboring countries, preventing the same vehicle from being scanned again and again at border crossings, and a rapid logistics transit corridor will be established.

Pekcan highlighted that they aim to set an example to all the customs authorities in the world by implementing another modern customs application with the project being developed by TBTAK.

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Vehicles passing through customs gate to be scanned via AI - Daily Sabah

Google held its big annual hardware event Tuesday in New York to unveil the Pixel 4, Nest Mini, Pixelbook Go, Nest Wifi, and Pixel Buds. It was mostly predictable because details about virtually every piece of hardware the company revealed at the event were leaked months in advance, but if Googles biggest hardware event of the year had an overarching theme, it was the many applications of on-device machine learning. Most of the hardware Google introduced includes a dedicated chip for running AI, continuing an industry-wide trend to power services consumers will no doubt enjoy, but there can be privacy implications too.

The new Nest Minis on-device machine learning recognizes your most commonly used voice commands to quicken Google Assistant response time compared to the first-generation Home Mini.

In Pixel Buds, due out next year, machine learning helps recognize ambient sound levels and increase or decrease sound the same way your smartphone dims or brightens when its in sunlight or shade.

Google Assistant on Pixel 4 is faster with an on-device language model. Pixel 4s Neural Core will power facial recognition for payment verification, Face Unlock, and Frequent Faces, which is AI that trains your camera to recognize the faces of people you photograph often and then coaches you on how to take the best picture.

Traditionally, edge deployment of on-device machine learning means an AI assistant can function without the need to maintain connection to the internet, an approach that can prevent the need to share user data online or collect the kind of voice recordings that became one of the most controversial privacy concerns for the better part of 2019.

Due to privacy concerns that stem from the routine recording of users voices, phrases like on-device machine learning and edge computing have become synonymous with privacy. Thats why a handful of edge assistants like Snips have made privacy a selling point.

For Googles many AI services, some like speech recognition powered by the Neural Core processor can entirely operate on-device, whereas others like the new Google Assistant require connecting to the cloud and sending your data back to the Google mothership.

Today, on-device AI for Google hardware is primarily meant to provide speed gains, Google Nest product manager Chris Chan told VentureBeat.

Tasks like speech recognition and natural language processing can be completed on-device, but they still need the cloud to deliver personalization and stitch together an ecosystem of smart home devices and streaming services like YouTube or Spotify.

Its a hybrid model, Chan said. If you focus too much on commands existing only on that single device, the user then doesnt benefit from the context of that usage to even other devices, let alone say Nest or Google services when theyre on the go, when theyre in the car, and other environments, Chan said.

In the case of on-device ML for Nest Mini, you still need an internet connection to complete a command, he said.

There are other architectures we could definitely explore over time that might be more distributed or based in the home, but were not there yet, Chan said.

The hybrid approach, as opposed to edge computing that can operate offline, raises the question: The package is powerful, but why not go all the way with an offline Google Assistant?

The answer may lie in that controversial collection of peoples voice data.

Leaders of the global smart speaker market and AI assistant market have moved in unison to address peoples privacy concerns.

In response to controversy over humans reviewing voice recordings from popular digital assistants like Siri, Cortana, Google Assistant, and Alexa, Google and Amazon both introduced voice commands to allow people to delete voice recordings every day. They also extended to users the ability to automatically remove voice data every three months or every 18 months.

So why make it easy to delete data but choose three months or 18 months?

When VentureBeat asked Alexa chief scientist Rohit Prasad this question, he said that Amazon wants to continue to track trends and follow seasonal changes in queries, and theres still more work to do to improve Alexas conversational AI models.

A Google spokesperson also said the company keeps data to understand seasonal or multi-season trends, but that this could be revisited in the future.

In our research, we found that these time frames were preferred by users as theyre inclusive of data from an entire season (a three-month period) or multiple seasons (18 months), the spokesperson said.

Chan said Google users may find more privacy benefits from on-device machine learning in the future.

Its our hope that over the coming years that things go entirely local, because then youre going to get a massive speed benefit, but were not there yet, he said.

As conversational computing becomes a bigger part of peoples lives, why and when tech giants connect assistants to the internet are likely to play a role in shaping peoples perceptions of edge computing and privacy with AI. But if the competition between tech giants ever becomes about making smart home usage more private to meet consumer demand, then consumers can win.

As always, if you come across a story that merits coverage, send news tips toKhari JohnsonandKyle Wiggers and be sure to bookmark our AI Channeland subscribe to theAI Weekly newsletter.

Thanks for reading,

Khari Johnson

Senior AI staff writer

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AI Weekly: Why Google still needs the cloud even with on-device ML - VentureBeat

Hyundai Motor Group said Oct. 21 it has developed an artificial intelligence-based autonomous driving technology and will apply it in its models.

The South Korean automaker has developed the smart cruise control-machine learning technology, in which the vehicle analyzes the drivers driving patterns and allows partial autonomous driving under the smart cruise control function, it said in a statement.

It is the first time for a carmaker to develop an AI-based self-driving technology in the worlds automobile industry, Hyundai said.

To use the SCC function, which is a core technology of the groups advanced driver assistance system, the driver has to set the driving speed, distance from other vehicles and other conditions, it said.

The SCC-ML technology allows Level 2.5 autonomous driving technology in a vehicle, the statement said.

Hyundai Motor and its affiliate Kia Motors said they plan to gradually apply the technology to their new models.

By Ram Garikipati and newswires (ram@heraldcorp.com)

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Hyundai develops AI-based self-driving tech - The Investor

Metatron AI

iPR Software AI for PR & Marketing Digital Publishing and DAM Solutions

iPR Software

Metatron AI to Launch at PRSA 2019 International Conference and will Revolutionize PR and Marketing Publishing & Sharing

LOS ANGELES, CA, Oct. 20, 2019 (GLOBE NEWSWIRE) -- via NEWMEDIAWIRE iPR Software, the leader in Online Newsrooms, Digital Publishing, Digital Asset Management (DAM) solutions, and customized integrated solutions, announced its largest technology rollout to date at Public Relations Society of Americas International Conference in San Diego, California. With the launch of Metatron, iPR Softwares new application empowers Artificial Intelligence (AI) cloud capabilities as well as integrating the power of machine learning into DAM and customized software platforms to increase productivity and corporate asset sharing across multiple customer ecosystems. This latest software release further advances the company's vision for clients to publish their news and information to Traditional and Social media channels and better engage their B2B & B2C audiences while increasing traffic to their branded media and corporate assets.

Leading organization's today are utilizing cloud applications to access the latest technology with encryption algorithms they can securely manage, publish, and share rich branded media content. Metatron introduces core, cloud-based software features that enable customers to securely publish and share key digital media and corporate assets, target practical enterprise use cases, increase workflow efficiencies, and automate mundane tasks to reduce data and storage errors.

iPR Softwares AI capabilities places the power of machine learning into the hands of PR & Marcom professionals virtually anywhere at anytime, said JD Bowles, President and CEO of iPR Software. Metatron creates a dynamic central hub for organizing, storing, and sharing rich assets, while easily connecting to other enterprise software solutions, managing infrastructure and ecosystem challenges for companies of all sizes.

Metatrons AI capabilities delivers next-generation technology to help enterprise-level organizations transform their PR and Marketing global strategies. iPR Softwares proprietary platform is powered by customized algorithms that utilize the worlds foremost big-data giants in Google and Amazon (AWS). Metatrons cloud-based platform offers newsrooms, digital publishing, and DAM platforms access to the worlds largest collections of curated visual assets so suggested metadata descriptions can optimize internal storage, sharing and distributions to online, traditional, and social media networks. These enhanced publishing and sharing tools are integrated into iPR Softwares industry-leading statistics and measurement portal, that enable clients to increase audience engagement and greater ROI.

Metatrons Artificial Intelligence capabilities are part of iPR Softwares mission to invest in revolutionary technologies, said Bowles. We are excited to implement game-changer technology to further innovate our industry-leading products and proprietary software and unlock the advantage in information management for our global customer base.

About iPR Software

iPR Software provides Interactive, Digital Publishing, DAM, and Customized Software solutions that leverage cloud, mobile and social media technologies to reinvent the way Public Relations and Marketing professionals do business. Since 2008, iPR Software has helped companies with their PR and Marketing publishing, sharing, distribution and cloud needs. As a software solutions provider, iPR Software serves leading global organizations like Xerox, Mattel, Northrop Grumman, NVIDIA, and the American Heart Association.

iPR Software Inc., also owns and operates NewMediaWire, Inc., which distributes thousands of press releases on behalf of publicly traded companies, private corporations, and nonprofits that directly connect them to Media outlets, Bloggers, Investors, and Influencers, both domestically and internationally.

iPR Software combines their proprietary platforms with innovative approaches that deliver collaborative, result-oriented customized integrated solutions to its clients. iPR Software also has helped companies of all sizes transform their business technologies by providing world-class customer service that insures that their client experience is unmatched. With global offices in the U.S., Asia, and Europe, iPR Software has an ever-increasing client base ranging from Fortune 100 companies, to nonprofits, government agencies, and small-to-mid size organizations.

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iPR Software Introduces the First Artificial Intelligence Application for Online Newsrooms and Digital Publishing - GlobeNewswire

Can artificial intelligence ever boost productivity of firms and industries the way the PC and ... [+] networking did in the '80s and '90s?

A big pastime of economists in the 1980s and 1990s was trying to gauge how much corporate and industrial productivity would benefit from the then-novel phenomena of personal computers, workgroup servers, and computer networking.

At first it was hard to see, but in time, economists did indeed find evidence that information technology contributed to boosting economic productivity.

Its too soon to expect to see data showing a similar boom from artificial intelligence, todays big IT revolution. The technology is just becoming industrialized, and many companies have yet to even try to use things such as machine learning in any significant way.

But its not too soon to speculate. Theres no question companies will increasingly use AI technologies of various sorts. AI is now well on its way to being part of how companies function. Every company has tons of data to analyze, and that analysis can benefit from even simple machine learning techniques. And companies have processes, from HR to accounting to sales, that can make use of automation that AI can bring.

Will all that show up in the numbers around output per employee and such, the measures of productivity?

Though it cant be ruled out, a couple big obstacles stand in the way of AI having an effect on productivity similar to the PC era.

One issue is that AI is dominated by the companies that are already among the most productive in the world. As MIT economist David Autor and colleagues have written, wealth is increasingly concentrated in the hands of what they term superstar firms, a situation of winner take most, where a small number of firms gain a very large share of the market, firms that are the more productive ones.

Those companies include Google and Facebook, and others that, Autor and colleagues show, are much more efficient in terms of their labor force. Many of the canonical superstar firms such as Google and Facebook employ relatively few workers compared to their market capitalization because their market value is based on intellectual property and a cadre of highly-skilled workers.

Google, Facebook, Apple, Amazon and Microsoft, the largest tech companies in the world, the superstar firms, are precisely the ones that already dominate artificial intelligence globally, the companies at the forefront of deep learning and other forms of cutting-edge AI. In a sense, AI is being used to reinforce productivity that is already vastly above normal.

At the same time, something unfortunate has befallen all the non-superstar firms in the world. Back in the 1980s and 1990s, PCs and related technology were a broad global trend benefitting any company that bought PCs, servers and networking. Productivity was theoretically available to all.

With the death of Moores Law, the decades-long rule of progress in the semiconductor industry, there is less and less technology improvement thats broadly available in a direct way to every firm. Fundamental research has contracted across the technology industry, and much of what innovation happens is increasingly concentrated in the R&D labs of those same superstar firms.

As Carnegie Mellon researchers Hassan N. Khan, David A. Hounshell, and Erica R. H. Fuchs wrote in Nature magazine last year, as advances in semiconductors slow, and downstream firms increasingly pursue application- or domain-specific innovations, technological progress will be increasingly unevenly distributed.

That uneven distribution is in contrast to the industry-wide benefits of advances in the underlying transistor technology in prior decades.

With superstar firms dominating AI, and broad tech progress no longer evenly distributed, how will AI contribute to a boom? Perhaps it will happen indirectly, a process of trickle-down productivity, as ordinary firms adopt the AI technologies provided by Google and Microsoft and Amazon in the cloud.

Even if productivity doesnt immediately improve at every firm, improvements could still materialize inside of industries, and as a national or global phenomenon.

Its important to remember that productivity can take time to materialize. Back in 1987, Nobel Prize-winning economist Robert Solow was the first scholar to point out the apparent absence of IT-led productivity growth. You can see the computer age everywhere but in the productivity statistics, he famously wrote. It took another decade or so, but eventually the numbers did show progress.

An AI boom is possible; certainly, it shouldnt be ruled out. But market concentration and a slowdown in tech innovation broadly speaking will make it more challenging to achieve than was the case for technology revolutions of the past.

Read the rest here:

Will There Be An AI Productivity Boom? - Forbes

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For all the talk about how artificial intelligence could transform what happens in the classroom, AI hasnt yet lived up to the hype.

AI involves creating computer systems that can perform tasks that typically require human intelligence. Its already being experimented with to help automate grading, tailor lessons to students individual needs and assist English language learners. We heard about a few promising ideas at a conference I attended last week on artificial intelligence hosted by Teachers College, Columbia University. (Disclosure: The Hechinger Report is an independent unit of Teachers College.)

Shipeng Li, corporate vice president of iFLYTEK, talked about how the Chinese company is working to increase teachers efficiency by individualizing homework assignments. Class time can be spent on the problems that are tripping up the largest numbers of students, and young people can use their homework to focus on their particular weaknesses. Margaret Price, a principal design strategist with Microsoft, mentioned a PowerPoint plug-in that provides subtitles in students native languages useful for a teacher leading a class filled with young people from many different places. Sandra Okita, an associate professor at Teachers College, talked about how AI could be used to detect over time why certain groups of learners are succeeding or failing.

But none of these artificial intelligence applications are particularly wide-reaching yet, the transformation of every aspect of the traditional learning environment which will usher in a bold new era of human history that promoters have imagined.

There is also plenty of reason to worry about what might happen as tech developers accelerate efforts to bring artificial intelligence into classrooms and onto campuses.

Paulo Blikstein, an associate professor at Teachers College, drew laughs by talking about Silicon Valleys public relations coup in getting us so excited about technologys promise that we happily parted with our private data, only to learn much later of the costs. A handful of tech CEOs caused enormous harm to our society, he said. I dont want that to happen in education yet again. Stavros Yiannouka, chief executive of the World Innovation Summit for Education (WISE), a project of the Qatar Foundation, and a panel moderator, agreed that there are great risks in letting artificial intelligence loose in classrooms. He pointed out, You dont need to have sinister objectives or plans for world domination to get things horribly wrong. Andre Perry, a fellow at the Brookings Institution and a Hechinger contributor, talked about how tech companies may cement racismand other biases into algorithms unless they employ diverse teams and consciously fight against inequities.

As Blikstein noted, AI educational applications come in two types tools that involve computers shaping how learning happens, and those that engage students in using AI to code and program. In a panel moderated by my colleague Jill Barshay, Stefania Druga, a PhD candidate at the University of Washington, discussed a platform shed created called Cognimates. It enables children to use artificial intelligence to train and build robots.

Druga talked about how kids first assumed the robots were super brainy. But once students learned how to train a robot, she said, their perception goes from, its smarter than me to, its not smart, significantly. We see that kids become not only more critical of these technologies but also more fluent.

She mentioned the creative and unexpected projects students wanted to tackle, including building a chatbot that gave back-handed compliments (a concept that Druga, who grew up in Romania, wasnt initially familiar with). We need more silly instead of smart technologies, Druga said, that puts the focus on people and allows people to do what they do best. In her evaluations of Cognimates, she found that students who gained the deepest understanding of AI werent those who spent the most time coding; rather, they were the students who spent the most time talking about the process with their peers. That left me thinking that its from other humans that we tend to learn the most and peers and teachers will always play a central role in education.

Editors note: This story led off this weeks Future of Learning newsletter, which is delivered free to subscribers inboxes every other Wednesday with trends and top stories about education innovation. Subscribe today!

This story about artificial intelligence was produced byThe Hechinger Report, a nonprofit, independent news organization focused oninequality and innovation in education. Sign up forHechingers newsletter.

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Will AI really transform education? - The Hechinger Report