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Will AI Shape the Future?

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Science is breaking every boundary on a daily basis. We have witnessed many things that we never thought possible. In the last few years, the talks of investing and developing artificial intelligence are growing rapidly. Many scientists believe that AI is our next step of evolution and will have a major positive impact on our lives. Some believe otherwise. We wanted you to decide whether they are a good or a bad change, so we will discuss some of the pros from both sides.

Positive Changes

Let's start with some businesses that already implemented the use of AI. Online casinos have expanded rapidly in recent years and their technology proved to be cutting-edge without any flaws. They use AI to ensure fair play, random outcomes, privacy, and safety on their sites. If you are interested to find out more, just visit their site.

The main argument that the pro-AI activists have is that the technology excludes the possibility of an error. With artificial intelligence, we can replace the jobs where the robots are perfectly qualified to perform and focus our resources on other areas that need developing.

Robots are also much faster in calculating possible outcomes than humans and are better at decision-making. They would be capable of analyzing various outcomes of a certain situation and based on the gathered information, they would be able to bring the best decision.

In conclusion to all of this, AI can free humans of taking up all responsibilities, eliminates the need for people to do tedious tasks, and they completely dwarf the human intelligence in certain areas, like disaster response and smart weather casting.

Negative Changes

Like we mentioned earlier in this article, not every scientist is fond of the idea of AI existence. Like they say, even though the technology is built to help humans, in the end, it will be their demise. For example, when building robots to fight wars and save human lives, robots can always turn against us. Although this may sound like a great idea for a sci-fi movie, the possibility is always there. Robots do what we program them to do.

The demise doesn’t have to include wars, though. Artificial intelligence is programmed to do something beneficial, but the skeptic people believe that they will incorporate the Machiavelli philosophy, where the ends justify the needs. That means that the possibility of AI developing destructive methods to reach its goal is high.

And lastly, when people talk about robots performing jobs that we don't have to, you must remember that thanks to that job, a person can feed its family. The risk of robots taking over and increasing global unemployment is high. That means that the poverty rates will rise and human death tolls caused by starvation will go upwards. So, instead of helping us perform better, robots will create an environment where it’s hard for us to prosper.

What are your thoughts on AI and the use of robots soon? As you can see, many scientists are divided on this topic, but there is still time to research and finally conclude whether the pros outweigh the cons, or if it’s the other way around.

Author: Alex

Posted in Ai

Facebook wants you to trust AI, and it’s hiring for a Research group to get you to do just that – Thinknum Media

Beginning on August 20, 2019, Facebook ($NASDAQ:FB) began listing job openings for a new "Research"job categoryat the company that appears to be hiring scientists to research everything from blockchain in user experience, to augmented reality, to the way that humans think about artificial intelligence. So far, the company has listedmore than 100 positions for the group, with the apex of hiring in September.

The openings are spread throughout the Facebook organization, with a particular focus on artificial intelligence. Of the 70 job titles, 22 are focused on AI, 17 on UX (user experience), and three on AR/VR.

A "Visiting Scientist - Trust in AI" listing mentions a "Trust-in-AI-Research" team that is looking for a Ph.D. in machine learning and AI to "contribute research that can be applied to Facebook product development". Other AI positions are listed in the table below.

Title

Category

Chercheur en Intelligence Artificielle (Diplm de l'universit)

Research

Chercheur Invit, Intelligence Artificielle (Diplm de l'universit)

Research

Ingnieur de Recherche, Intelligence Artificielle (Diplm de l'universit)

Research

Postdoctoral Researcher, Artificial Intelligence (PhD)

Research

Program Manager, AI Programs

Research

Research Engineer, Artificial Intelligence (University Grad)

Research

Research Scientist, (Conversational AI Group)

Research

Research Scientist, AI

Research

Research Scientist, AI (EMEA)

Research

Research Scientist, AI (Montreal)

Research

Research Scientist, AI Research

Research

Visiting Researcher, Artificial Intelligence (PhD)

Research

Visiting Scientist - AI-Infra (US)

Research

Visiting Scientist - Facebook AI Applied Research (EMEA)

Research

Visiting Scientist - Facebook AI Applied Research (US)

Research

Visiting Scientist - Facebook AI Research (EMEA)

Research

Visiting Scientist - Facebook AI Research (Montreal)

Research

Visiting Scientist - Facebook AI Research (US)

Research

Visiting Scientist - Trust in AI (US)

Research

Visiting Scientist, AI

Research

Visiting Scientist, AI (EMEA)

Research

Visiting Scientist, AI (Montreal)

Research

Another curious hire is for an "Epitaxy Engineering Manager" for the new group's Augmented / Virtual Reality (AR/VR) team. Epitaxy involves the growth of crystals on the substrate,and this listing makes mention of advancing LED Expitaxy Materials Systems for AR Displays, an indication that Facebook and its Oculus group are creating new display technology.

Hiring for the new Research group appears spread across several Facebook offices, including its Menlo Park Headquarters, New York, Montreal, Redmond, Pittsburg, San Francisco, Seattle, Boston, Cork, and London.

Facebook's interest in the future of AI isn't surprising, and it already has a deep foothold in Virtual Reality. Understanding how humans perceive and interact with these technologies appears to be the first step in approachinghow the company will handleproduct releases that will use the technology.

Thinknum tracks companies using the information they post online - jobs, social and web traffic, product sales and app ratings - andcreates data sets that measure factors like hiring, revenue and foot traffic. Data sets may not be fully comprehensive (they only account for what is available on the web), but they can be used to gauge performance factors like staffing and sales.

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Facebook wants you to trust AI, and it's hiring for a Research group to get you to do just that - Thinknum Media

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AI tool suggests ways to improve your outfit – Futurity: Research News

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A new artificial intelligence system can look at a photo of an outfit and suggest helpful tips to make it more fashionable.

Suggestions may include tweaks such as selecting a sleeveless top or a longer jacket.

We thought of it like a friend giving you feedback, says Kristen Grauman, a professor of computer science at the University of Texas at Austin whose previous research has largely focused on visual recognition for artificial intelligence.

Its also motivated by a practical idea: that we can work with a given outfit to make small changes so its just a bit better.

The tool, named Fashion++, uses visual recognition systems to analyze the color, pattern, texture, and shape of garments in an image. It considers where edits will have the most impact. It then offers several alternative outfits to the user.

Researchers trained Fashion++ using more than 10,000 images of outfits shared publicly on online sites for fashion enthusiasts. Finding images of fashionable outfits was easy, says graduate student Kimberly Hsiao. Finding unfashionable images proved challenging. So, she came up with a workaround. She mixed images of fashionable outfits to create less-fashionable examples and trained the system on what not to wear.

As fashion styles evolve, the AI can continue to learn by giving it new images, which are abundant on the internet, Hsiao says.

As in any AI system, bias can creep in through the data sets for Fashion++. The researchers point out that vintage looks are harder to recognize as stylish because training images came from the internet, which has been in wide use only since the 1990s. Additionally, because the users submitting images were mostly from North America, styles from other parts of the world dont show up as much.

Another challenge is that many images of fashionable clothes appear on models, but bodies come in many sizes and shapes, affecting fashion choices. Next up, Grauman and Hsiao are working toward letting the AI learn what flatters different body shapes so its recommendations can be more tailored.

We are examining the interaction between how a persons body is shaped and how the clothing would suit them. Were excited to broaden the applicability to people of all body sizes and shapes by doing this research, Grauman says.

Grauman and Hsiao will present a paper on their approach at next weeks International Conference on Computer Vision in Seoul, South Korea.

Additional researchers from Cornell Tech, Georgia Tech, and Facebook AI Research contributed to the work.

Source: UT Austin

Original Study

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AI tool suggests ways to improve your outfit - Futurity: Research News

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Google researchers taught an AI to recognize smells – Engadget

The researchers created a data set of nearly 5,000 molecules identified by perfumers, who labeled the molecules with descriptions ranging from "buttery" to "tropical" and "weedy." The team used about two-thirds of the data set to train its AI (a graph neural network or GNN) to associate molecules with the descriptors they often receive. The researchers then used the remaining scents to test the AI -- and it passed. The algorithms were able to predict molecules' smells based on their structures.

As Wired points out, there are a few caveats, and they are what make the science of smell so tricky. For starters, two people might describe the same scent differently, for instance "woody" or "earthy." Sometimes molecules have the same atoms and bonds, but they're arranged as mirror images and have completely different smells. Those are called chiral pairs; caraway and spearmint are just one example. Things get even more complicated when you start combining scents.

Still, the Google researchers believe that training AI to associate specific molecules with their scents is an important first step. It could have an impact on chemistry, our understanding of human nutrition, sensory neuroscience and how we manufacture synthetic fragrance.

Google isn't alone. At an AI exhibit at London's Barbican Centre earlier this year, scientists used machine learning to recreate the smell of an extinct flower. In Russia, AI is being used to sniff out potentially deadly gas mixtures, and IBM is experimenting with AI-generated perfumes. Some have even toyed with using our sense of smell to reimagine how we design machine learning algorithms.

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Google researchers taught an AI to recognize smells - Engadget

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AI and Blockchain: Double the Hype or Double the Value? – Forbes

blockchain

Artificial Intelligence (AI) as a market is full of hype, with vendors, customers, and press all speaking breathlessly about the capabilities for AI in general and their offerings specifically. Likewise, blockchain is also a widely hyped market, with technology providers and customers claiming all sorts of capabilities that may or may not be possible. Combining AI and blockchain then must be double the hype? On the other hand, AI is providing real, tangible value in many myriad ways we talk about every day. Likewise, blockchain is starting to show value across a range of applications and industry. So, perhaps combining AI and blockchain will also show twice the value combined together.

The role of blockchain in the context of AI

Blockchain is a decentralized, distributed ledger of transactions that has elements of transparency, trust, verifiability, and something called smart contracts. Decentralized and distributed means that information that is stored across the network in such a way that each end point has access to the data without requiring access to a central server. The network is also distributed because the transactions happen at each end point without requiring centralized coordination. A ledger is a record of transactions. Blockchain records a ledger of interactions between two separate parties whether it be a financial exchange or even a chain of custody showing when things have changed hands over time.

Since every block in the blockchain contains a different piece of information that is encrypted or encoded, the blockchain can help guarantee trust and verifiability of data. The concept of the chain and block in blockchain is that each block has its own information and that information contains a link to the block before it, which develop the chain and provides a verifiable chain of custody. No individual actor can change the information in a block without invalidating the entire chain of information in a particular block and thus messing up the chain. Since the chain is distributed in myriad of other places and between other parties, to make changes to the chain, a consensus of all the parties would need to agree to the changes being made.

Adding to the concept of the blockchain are smart contracts, which are decentralized pieces of code that can be triggered when a specific chain of actions has been met. In this way, when you have two parties wanting to execute a secure, trusted transaction without the use of an intermediary this is what blockchain ideally would be used for.

So how can blockchain help with AI? The first benefit is that you can share machine learning models among all parties without an intermediary. A good example is with facial recognition software. If one device knows what a person looks like and uploads it to the chain, other devices hooked up also know what that person looks like. When other devices upload their own facial recognition data, the other devices will gain the ability to use that for the facial recognition model. Since this happens on the blockchain, there is no central control over facial recognition, and as such no one company owns or stores the data. This approach also allows for everyone to learn faster and collaboratively through the use of integrated AI with blockchain.

AI systems can also use blockchain to facilitate the sharing of data used across multiple models. A great example is the use of machine learning models for product recommendations in online retail. If an online store knows the preferences of one shopper, and then that customer goes to a different store website, these two sites can be connected through a blockchain to share trusted personalization information. This could potentially become a place of competition for smaller ecommerce sites that want to share with each other personalization information. Instead of each company gathering their own personalization data, they can share it amongst each other in a blockchain, providing competition to sites like Amazon and Walmart who have already developed their own data systems to gather this information about their customers. The benefit for this to the customer is that in exchange for sharing their information, they can get everything from better pricing to customized shopping experiences. This could also prevent data breaches if all information and payment systems are stored and shared through a blockchain rather than a centralized data server.

How blockchain can benefit AI

Not only can blockchains be used to share models and data, but blockchains can help serve a role as a master brain in a manner shared across multiple AI systems. If we can put all of these shared learning benefits and blockchain and AI together, the possibility may also be there to combine all of these things that can learn from their surroundings and then share that learning with all the AI systems on the network. A major benefit would be that no one owns it and theres no government control over the bot or the shared brain. It could potentially be unbiased because of the sheer amount of information coming in from different areas and different angles.

Another application is addressing the challenge of explainable AI. One of the more significant problems with deep learning is that there isnt a clear idea about what inputs result in what output and how that affected the whole sequence. If something goes wrong in a deep learning neural network, we dont have a clear idea of how to identify the problem and correct it. This is the problem of neural networks in effect being a black box without any real transparency or explainability. However, if we use blockchains, we can record how individual actions result in a final decision in a non-reputable manner, which allows us to go back and see where things went wrong and then fix the problem. The blockchain would be used to record events, such as autonomous vehicle decisions and actions in a way that will not be modified later. This can also increase trust since the blockchain element is unbiased and is just for storage and analysis, anyone can go in and see what has happened.

Finally, AI systems can be used to improve blockchains in general. Machine learning systems can keep an eye on what is happening in the blockchain. It can look for patterns and anomalies in the types of data being stored and actions being performed on a particular server and be used to alert users when something may be happening. The systems can look for normal behavior and flag what seems to be unusual. The AI systems can help keep blockchain more secure, more reliable, and more efficient.

While its quite possible that the worlds of AI and blockchain are full of hype, there are actual, tangible, realistic ways in which the two emerging technologies can be used in ways that benefit each other and provide real outcomes for those looking to implement the technologies today in their environments.

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AI and Blockchain: Double the Hype or Double the Value? - Forbes

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Google rolls out updates to AI Platform Prediction and AI Platform Training – VentureBeat

Googles AI Platform, a cloud-hosted service facilitating machine learning and data science workflows, today gained a new feature in backend models that tap powerful Nvidia graphics chips. In related news, Google debuted a refreshed model training experience that allows users to run a training script on any range of hardware.

For the uninitiated, AI Platform enables developers to prep, build, run, and share machine learning models quickly and easily in the cloud. Using built-in data labeling services, theyre able to annotate model training images, videos, audio, and text corpora by applying classification, object detection, and entity extraction. A managed Jupyter Notebook service provides support for a slew of machine learning frameworks, including Googles TensorFlow, while a dashboard within the Google Cloud Platform console exposes controls for managing, experimenting with, and deploying models in the cloud or on-premises.

Now, AI Platform Prediction the component of AI Platform that enables model serving for online predictions in a serverless environment lets developers choose from a set of machine types in Googles Compute Engine service to run a model. Thanks to a new backend built on Google Kubernetes Engine, theyre able to add graphics chips like Nvidias T4 and have AI Platform Prediction handle provisioning, scaling, and serving. (Online Prediction previously only allowed you to choose from one or four vCPU machine types.)

Additionally, prediction requests and responses can now be logged to Googles BigQuery, where they can be analyzed to detect skew and outliers.

As for AI Platform Training which allows data scientists to run a training script on a variety of hardware, without having to manage the underlying machines it now supports custom containers, letting researchers launch any Docker container so that they can train a model with any language, framework, or dependencies. Furthermore, AI Platform Training gained Compute Engine machine types for training, which allows for the piecemeal selection of any combination of CPUs, RAM, and accelerators.

Cloud AI Platform simplifies training and deploying models, letting you focus on using AI to solve your most challenging issues From optimizing mobile games to detecting diseases to 3D modeling houses, businesses are constantly finding new, creative uses for machine learning, wrote Cloud AI Platform product manager Henry Tappen in a blog post. With more inference hardware and training software choices, we look forward to seeing what challenges you use AI to tackle in the future.

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Google rolls out updates to AI Platform Prediction and AI Platform Training - VentureBeat

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A conversation on the future of AI. – Axios

The big picture: On Thursday morning, Axios' Cities Correspondent Kim Hart and Emerging Technology Reporter Kaveh Waddell hosted a roundtable conversation to discuss the future of AI, with a focus on policy and innovation.

The conversation touched on how to balance innovation with necessary regulation, create and maintain trust with users, and prepare for the future of work.

As AI continues to become more sophisticated and more widely used, how to provide regulatory guardrails while still encouraging innovation was a focal point of the discussion.

Attendees discussed balancing regulation and innovation in the context of global competition, particularly with China.

The conversation also highlighted who is most impacted by technological development in AI, and the importance of future-proofing employment across all industries. As AI is something that touches all industries, the importance of centering the human experience in creating solutions was stressed at multiple points in the conversation.

With the accelerating development of AI, creating and maintaining trust with users, consumers, and constituents alike was central to the discussion.

Thank you SoftBank Group for sponsoring this event.

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A conversation on the future of AI. - Axios

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AI Researchers Hate The "Terminator" Movies With a Passion – Futurism

Science Fiction

Theres a new Terminator movie coming out and artificial intelligence researchers really hope you dont see it.

The issue lies in the films misrepresentation of AI, several experts told BBC News in a fascinating new story, which they worry could stir up irrational fears about the tech the same way Jaws once made moviegoers afraid to go in the water.

[The films] paint a picture which is really not coherent with the current understanding of how AI systems are built today and in the foreseeable future, AI pioneer Yoshua Bengio told the outlet.

The reality is that todays AI systems are hyper-specialized, with programmers often designing an AI to excel at just one task, such as playing a board game. Researchers havent come close to creating a Terminator-level artificial general intelligence capable of acting beyond the control of its creator.

We are very far from super-intelligent AI systems, Bengio told BBC News, and there may even be fundamental obstacles to get much beyond human intelligence.

Still, while todays AI systems may not resemble those of the Terminator films, that doesnt mean the public shouldnt consider the harm AI is already inflicting upon society and the dangers lurking ahead.

AI is already helping us destroy our democracies and corrupt our economies and the rule of law, Joanna Bryson, an AI expert at the University of Bath, told BBC News, adding that it is good to get people thinking about the problems of autonomous weapons systems.

READ MORE: Why Terminator: Dark Fate is sending a shudder through AI labs [BBC News]

More on autonomous weapons: Experts: Itd Be Relatively Easy to Deploy Killer Robots by 2021

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AI Researchers Hate The "Terminator" Movies With a Passion - Futurism

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Why per-seat pricing needs to die in the age of AI – TechCrunch

Instead, create pricing models that maximize product usage and product value

Pricing is the most important, least-discussed element of the software industry. In the past, founders could get away with giving pricing short shrift under the mantra, the best product will ultimately win. No more.

In the age of AI-enabled software, pricing and product are linked; pricing fundamentally impacts usage, which directly informs product quality.

Therefore, pricing models that limit usage, like the predominant per-seat per month structure, limit quality. And thus limit companies.

For the first time in 20 years, there is a compelling argument to make for changing the way that SaaS is priced. For those selling AI-enabled software, its time to examine new pricing models. And since AI is currently the best-funded technology in the software industry by far pricing could soon be changing at a number of vendors.

Per-seat pricing makes AI-based products worse. Traditionally, the functionality of software hasnt changed with usage. Features are there whether users take advantage of them or not your CRM doesnt sprout new bells and whistles when more employees log in; its static software. And since its priced per-user, a customer incurs more costs with every user for whom its licensed.

AI, on the other hand, is dynamic. It learns from every data point its fed, and users are its main source of information; usage of the product makes the product itself better. Why, then, should AI software vendors charge per user, when doing so inherently disincentivizes usage? Instead, they should design pricing models that maximize product usage, and therefore, product value.

AI-enabled software promises to make people and businesses far more efficient, transforming every aspect of the enterprise through personalization. Software tailored to the specific needs of the user has been able to command a significant premium relative to generic competitors; for example, Salesforce offers a horizontal CRM that must serve users from Fortune 100s to SMBs across every industry. Veeva, which provides a CRM optimized for the life sciences vertical, commands a subscription price many multiples higher, in large part because it has been tailored to the pharma users end needs.

AI-enabled software will be even more tailored to the individual context of each end-user, and thus, should command an even higher price. Relying on per-seat pricing gives buyers an easy point of comparison ($/seat is universalizable) and immediately puts the AI vendor on the defensive. Moving away from per-seat pricing allows the AI vendor to avoid apples-to-apples comparisons and sell their product on its own unique merits. There will be some buyer education required to move to a new model, but the winners in the AI era will use these discussions to better understand and serve their customers.

Probably the most important upsell lever software vendors have traditionally used is tying themselves to the growth of their customers. As their customers grow, the logic goes, so should the vendors contract (presumably because the vendor had some part in driving this growth).

Tethering yourself to per-seat pricing will make contract expansion much harder.

However, effective AI-based software makes workers significantly more efficient. As such, seat counts should not need to grow linearly with company growth, as they have in the era of static software. Tethering yourself to per-seat pricing will make contract expansion much harder. Indeed, it could result in a world where the very success of the AI software will entail contract contraction.

Here are some key ideas to keep top of mind when thinking about pricing AI software:

This is the same place to start as in static software land. (Check out my primer on this approach here.) Work with customers to quantify the value your software delivers across all dimensions. A good rule of thumb is that you should capture 10-30% of the value you create. In dynamic software land, that value may actually increase over time as the product is used more and the dataset improves. Its best to calculate ROI after the product gets to initial scale deployment within a company (not at the beginning). Its also worth recalculating after a year or two of use and potentially adjusting pricing. Tracking traditionally consumer usage metrics like DAU/MAU becomes absolutely critical in enterprise AI, as usage is arguably the core driver of ROI.

While ROI is a good way to determine how much to charge, do not use ROI as the mechanism for how to charge. Tying your pricing model directly to ROI created can cause lots of confusion and anxiety when it comes time to settle up at year-end. This can create issues with establishing causality and sets up an unnecessarily antagonistic dynamic with the customer. Instead, use ROI as a level-setting tool and other mechanisms to determine how to arrive at specific pricing.

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Why per-seat pricing needs to die in the age of AI - TechCrunch

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One Partner’s Experience Using AI To Measure Customer Satisfaction – CRN: The Biggest Tech News For Partners And The IT Channel

Cloudbakers, a system integrator focused on supporting cloud services and migration for its 500 clients, began measuring customer satisfaction using artificial intelligence a few years ago and has since successfully identified patterns and smoothed out problem areas.

"We see our job as 50 percent human, 50 percent tech. For us, monitoring our customer's happiness is a critical success factor," Eric Lannert, CTO of Chicago-based Cloudbakers, told MSPs at The Channel Company's NexGen 2019 conference.

Solution providers can harness AI technologies like sentiment analysis gain deeper insights into topics that are trending, and positive and negative feedback from their customers, Lannert said.

[Related: AI Explosion: How Solution Providers Are Cashing In Big]

Cloudbakers is a Google Cloud Premier Partner, so the firm selected Google's machine learning natural language API to automatically compute sentiment analysis for its cloud licenses it resells. The industry average response rate to satisfaction surveys typically runs around 30 percent, but CloudBakers wanted to see what would happen if they used AI APIs to innovate on the data it was collecting from its ticket system.

Google's sentiment analysis gave Cloudbakers a quantitative, emotional scoring on a 1-10 basis of text from their customers through the tickets by recognizing specific words and phrases.

"As a reseller, provisioning is usually a sticking point for customers, and we learned that amazingly, that was the most positive with our customers," Lannert said. "Interestingly from a problem-solving standpoint, PDFs were having an issue, which turned out to be a common theme across clients dealing with cloud syncing. The technology is helping us diagnose customer patterns across many different clients."

Today, Cloudbakers is considering reselling AI to track satisfaction as an offering for its clients, rather than only continuing to use it in-house, Lannert said. The firm is talking to its customers to learn which insights they'd pay more or less for, and how much analysis will the customer expect of Cloudbakers.

"This is giving us a good idea to build a pretty big offering set, he said.

Vistem Solutions, Inc. (VSI), an Irvine, Calif.-based MSP, specializes in supporting hospitality and labor union customers, as well as the shipping industry on the Pacific Coast. VSI has its own development team, so the firm can do data integration and software modifications to help disparate systems communicate with each other. VSI is interested in using AI to measure customer satisfaction for its hospitality clients to use during their guests' transactions, not after their experience is over, said Keith Nelson, vice president of technology for VSI.

"Customers like convention centers are very big into guest satisfaction, so I'm interested in tracking satisfaction and surveying along the process, versus at the end," he said. "Having a customer say they're mad afterward doesn't accomplish much."

VSI is in the process of evaluating several new cloud partners for development, including Amazon Web Services and Microsoft Azure, Nelson said.

MSPs interested in offering an AI-based solution to track satisfaction should take advantage of cloud platforms for building scripts that allow these solutions to be deployed in one click and automatically billed, lannert said. Channel partners will also have to identify the right customers within their base that will be open evolving their businesses, he added.

"Your potential early adoptions are people who are interested in machine learning," he said. "This is an example of moving from the procurement of hardware to the procurement of solutions, with the same level of automation and customer experience surrounding it."

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One Partner's Experience Using AI To Measure Customer Satisfaction - CRN: The Biggest Tech News For Partners And The IT Channel

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AI Spotlight: Paul Scharre On Weapons, Autonomy, And Warfare – Forbes

Paul Scharre / Senior Fellow & Director at CNAS

Paul Scharreis a Senior Fellow and Director of the Technology and National Security Program at the Center for a New American Security. He is the award-winning author ofArmy of None: Autonomous Weapons and the Future of War, which won the 2019 Colby Award and was named one of Bill Gates top five books of 2018.

Aswin Pranam: To start, what classifies as an autonomous weapon?

Paul Scharre: An autonomous weapon, quite simply, makes its own decisions of whom to engage in the battlefield. The core challenge is in figuring out which of those decisions matter. For example, modern-day missiles and torpedoes maneuver on their own to course-correct and adjust positioning. Do these decisions matter on a grand scale? Not so much. But munitions that can make kill decisions on their own, without human supervision, matter a great deal.

Pranam: Why should the average citizen care about autonomous weapons?

Scharre: Everyone will have to live in the future we're building, and we should all have a personal stake in what that future looks like. The question of AI being used in warfare isnt a question of when, but rather a question of how. What are the rules? What is the degree of human control? Who sets those rules? There is a real possibility that militaries transition to a world in which human control over war is significantly reduced, and that could be quite dangerous. So, I think engaging in broad conversation internationally and bringing together nations, human rights groups, and subject matter experts (lawyers, ethicists, technologists) to have a productive dialogue is necessary to chart the right course.

Pranam: People concerned about the destructive power of AI weapons want to halt development completely. Is this a realistic solution?

Scharre: We cant stop the underlying technology from being developed because AI and automation are dual-use. The same sensors and algorithms that prevent a self-driving car from hitting pedestrians may also enable an autonomous weapon in war. The basic tools to build a simple autonomous weapon exist today and can be found freely online. If a person is a reasonably competent engineer and has a bit of free time, they could build a crude autonomous drone that could inflict harm for under a thousand dollars.

The open question is still what militaries choose to build with regards to their weapons arsenal. If you look at chemical and biological weapons as a parallel, certain rogue states develop and use them, but the majority of civilized countries have generally agreed not to move forward with development. Historically, attempts to control technology has been a mixed bag, with some successes and many failures.

Pranam: In the United States, the International Traffic in Arms Regulations (ITAR) compliance framework controls & restricts the export of munitions and military technologies. Do you believe AI should fall under the export restricted category?

Scharre: This is an area with a lot of active policy debate in both Washington DC and the broader tech community. Personally, I dont see it as being realistic in most cases. However, theres room for non-ITAR export controls that restrict the sale of AI technologies to countries engaged in human rights abuses. We shouldnt have American enterprises or universities enabling foreign entities that will repurpose the technology to suppress their citizens.

By and large, as far as the access to research is concerned, the AI world is very open. Papers are published online and breakthroughs are freely shared, so it is difficult to imagine components of AI technology being controlled under tight restrictions. If anything, I could see sensitive data sets being ITAR controlled, along with potentially specialized chips or hardware used exclusively for military applications, if they were developed.

Pranam: Conversations around AI and morality generally go hand-in-hand. In your research, have you encountered any examples of AI weapons today that use ethical considerations as an input to decision making?

Scharre: I havent, and I dont fully agree with the characterization that AI needs to engage in moral reasoning in the human sense. We should focus attention on outcomes. We want behavior from AI that is consistent with human ethics and values. Does this mean that machines need to reason or understand abstract ethical concepts? Not necessarily. We do, however, need to control the manifestation of external behavior in machines and reassert human control in cases where ethical dilemmas present themselves. Commercial airliners use autopilots to improve safety. It doesnt need to have moral reasoning programmed in to get to a moral outcome a safe plane flight. In a similar vein, self-driving cars will pilot themselves with the primary outcome of driving safely. Programming in ethical scenarios to manage variations of the trolley problem is largely a red herring.

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AI Spotlight: Paul Scharre On Weapons, Autonomy, And Warfare - Forbes

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Spreading human rights around the world, one AI at a time? – Reuters

LONDON (Thomson Reuters Foundation) - British billionaire Richard Branson, founder of Virgin Atlantic Airways and a campaigner for LGBT+ rights, cannot be in all places at all times.

Technology could soon change that.

Uncannily familiar in appearance and voice, avatars of Branson and two human rights activists displayed on tablet devices were unveiled at a youth summit in London on Friday.

The digital doppelgangers use pre-recorded phrases to interactively engage in conversations with people about social causes like climate change through a mobile application designed by technology company AI Foundation.

In theory, that means an imprisoned human rights activist could continue to engage with others through their avatar.

You will never be able to silence anyone again, said AI Foundations CEO Lars Buttler at the One Young World conference.

The animated renderings of Branson, a Colombian kidnapping survivor and a North Korean refugee addressed a packed auditorium in central London and asked each other questions aboutdemocracy and forgiveness.

Laura Ulloa, the 28-year-old Colombian activist which one of the avatars is modeled on, told the conference the technology allows her to be in all the places I cant be and to have one-on-one conversations that could change peoples minds through empathy.

Other kinds of artificial intelligence including robots, holograms and AI dolls have yet to be mass produced but are depicted in popular culture including in the 2019 novel by British author Ian Mcewan, Machines Like Me.

Rights groups are examining how to use artificial intelligence to monitor abuses like the death penalty but others have raised concerns about the dangers posed by avatars.

Such avatars come with clear risks, said Edin Omanovic, of UK-based surveillance monitoring group Privacy International.

You have to hand over a huge amount of sensitive information to make the program work and trust the company to keep this data secure from hackers and not to monetize it by sharing your data with third parties.

Co-founder of the One Young World summit, David Jones said: bad people have always done bad things with new technology but were trying to use this technology to drive positive change while protecting it as much as we can.

Editing by Tom Finn Please credit Thomson Reuters Foundation, the charitable arm of Thomson Reuters, that covers humanitarian news, women's rights, trafficking, property rights, and climate change. Visit http://www.trust.org

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Spreading human rights around the world, one AI at a time? - Reuters

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The AI Foundation Reveals Groundbreaking Technology to Drive Positive Social Change with the Power of Your Own AI – Business Wire

LONDON--(BUSINESS WIRE)--The AI Foundation, the leader in AI dedicated to benefit humanity, revealed groundbreaking AI that can accelerate positive change at Future Day during the One Young World Summit. The company showed how the worlds young leaders, activists, and their role models can champion their purpose and scale their impact through their own AI.

On stage at One Young World, The AI Foundation revealed AI versions of Laura Ulloa, a 28-year-old Colombian kidnapping survivor who promotes forgiveness to victims, violent offenders, and the world, Geum Hyok Kim, a 27-year-old North Korean refugee and activist who plans to engage 10k influential North Koreans to advocate for democracy, and Sir Richard Branson, who is dedicated to addressing the problems of the world by counseling young leaders.

The AI Foundations mission is to help move the world forward through the power of AI. In order to ensure that AI is in service of improving the world, the company has a special structure that places responsible AI practices at its core. The company also has a non-profit, entirely dedicated to research, products and initiatives, under the brand Reality Defender, to mitigate the risks of our inevitable future of living with AI.

The AI Foundations vision is to give the seven billion people on the planet their own AI that shares their values and goals," said Dr. Lars Buttler, CEO & co-founder, The AI Foundation. The future of social change is AI. If everyone has their own AI we can create a better, more just, and more abundant future. We are thrilled to reveal AI of these leaders with incredible messages at the One Young World Summit, where emerging leaders and todays role models come together to tackle humanitys toughest problems.

By giving AI to everyone, The AI Foundation is pioneering what it calls Personal Mediadirect one-to-one conversations at limitless scale. Through unlocking limitless direct one-on-one conversations, the AI Foundation believes we can educate, present different points of view, share personal stories, build more empathy, more awareness, and more collaboration to overcome issues that affect us all.

Laura and Geum Hyok are only two of the incredible delegates that were selected to represent the young leaders with the potential to change the world for the better. The AI Foundation aims to amplify their reach through their own AIs that look, speak, and act like these young leaders, to magnify their positive impact on the world. Their AIs belong to them completely, with their eyes, voices, values, and memories. The AIs speak like them, on their behalf, as they represent the thoughts and experiences of each one of the delegates. Powered by their own AI, these activists can have unlimited one-to-one conversations with people, and ultimately inspire, motivate, and connect with their communities at greater scale.

Today, The AI Foundations co-founder and CEO, Dr. Lars Buttler was joined by Twitters co-founder Biz Stone, and co-founder of the One Young World Summit, David Jones, to present the three AIs and showcase how they can make a bigger impact. The AI Foundation also committed to building AIs for additional delegates so they too can extend the power of their potential and accelerate positive impact.

Together we can make AI a triumphnot of technology, but of humanity," said Biz Stone, co-founder of Twitter. "Im inspired by what people do with Twitter, but this is next-level. Having your own AI? The power of your own purpose and principles at scale? Unbelievable. AI will define the next era of social change. But it falls on all of usand especially on youto fulfill the true promise of AI which is to amplify the best traits of humanity. The AI Foundation is the most advanced, and the most socially responsible leaders in the field of Artificial Intelligence.

The potential of AI is so incredibly huge: it will change business, marketing and the internet itself over the next decade, said David Jones. It also comes with some real concerns. The big mistake most of today's established tech platforms made was to totally underestimate the potential for their misuse. AI Foundation is the first tech platform with an incredibly responsible approach as its starting point, providing tools to head off potential misuse including Reality Defender, which allows people to detect deep fakes. And they are setting out to use their technology as a force for good to help people scale their purpose and impact.

One Young World is the global forum that identifies, promotes and connects young leaders to create a better world, with more responsible, more effective leadership. Founded by Kate Robertson and David Jones the Summit is convening 2,000 delegates from 190+ countries to work alongside political and humanitarian leaders, including former UN Secretary General Ban Ki-moon, Amnesty International Secretary General Kumi Naidoo, President Mary Robinson, Sir Richard Branson, JK Rowling, Ellie Goulding and HRH Meghan Markle. It is the most international event the UK has hosted since the 2012 Olympic and Paralympic Games.

About the AI Foundation

The AI Foundation is dedicated to moving the world forward by giving each of us our own AI that shares our personal values and goals. The company has brought together many of the worlds top innovators, AI scientists, engineers, and investors from Silicon Valley, Hollywood and Madison Avenue, all united around the idea of making the power of AI available to all, while protecting us from its dangers. The AI Foundations non-profit entity focuses on tools for detection and protection, while AI Foundations for-profit creates foundational technologies, products and services to unlock the full human potential.

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The AI Foundation Reveals Groundbreaking Technology to Drive Positive Social Change with the Power of Your Own AI - Business Wire

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7 AI Stocks to Buy to Profit from the Recent Tech Correction – Investorplace.com

Artificial intelligence has slowly moved from a science fiction topic to something that has a huge impact on peoples livelihoods and the world around us. Still, its common for many companies to use AI as a simple buzzword. But it would be a mistake to think that AI is just jargon though.

At this point, the power of artificial intelligence is growing exponentially. As such, look for AI-driven technologies to make a massive impact over the next decade. That will, of course, have a major effect on the stock market. Right now, many tech investors are primarily focused on cloud and software-as-a-service stocks. And with good reason those have been booming.

But with all the data accumulating online, its only a matter of time until more powerful AI turns that data into new understanding and new profits. Heres seven stocks to harness the power of artificial intelligence in your portfolio.

Source: Piotr Swat / Shutterstock.com

Alphabet (NASDAQ:GOOG, NASDAQ:GOOGL) is far from a pure play on artificial intelligence. The company still pays the bills with search advertising, after all. And its dominant Android operating system will continue reaping rewards for Alphabet for many years to come.

That said, if you want a company that undoubtedly will be a major part of artificial intelligence in coming years, look no further. Google launched Google AI in 2017 as a separate entity to show the importance of AI to the companys future.

Already, Alphabet has made moves with DeepMind, its roughly 700-employee division that has achieved things such as beating humans at the board game Go. Recently, Alphabets artificial intelligence topped humans in deciphering ancient Greek text. Turning to more practical matters, Google is using AI to help customers solve important although perhaps mundane tasks such as filling in online forms automatically or responding better and faster to voice-inputted questions.

All this plays back into Alphabets core products, as well. The better Google Search is, for example, the more advertising revenue Google will be able to bring in. Artificial intelligence is at the core of GOOGL stock, and its further advances in the field should help power profits across the companys other lines of business.

Source: Laborant / Shutterstock.com

International Business Machines (NYSE:IBM) isnt having a great year. Its not had a great past five years in fact. IBMs stock price has stagnated as the company has struggled to adapt to a changing tech market. The companys hugeRed Hat acquisition will be critical in trying to get its cloud division going again.

That said, while IBM has had its struggles, you cant deny its excellent positioning in AI. IBMs Watson is one of the most exciting applications. Not only does it have its high-profile appearances, such as competing in game shows and chess matches, it is also being used for chat applications, healthcare, weather prediction and assisting teachers.

As Watson is able to answer questions in natural language, the range of potential uses is immense. And IBM is constantly upgrading Watsons capabilities. Just this week, it added new functionality to Watson Assist, Watson Discovery and a drift function that can show when the AIs prediction is moving away from actual real world results.

Some investors have grown fed up with IBM, and its not hard to see why. In a raging bull market IBM stock has offered little besides its dividend. But dont let bad past performance make you write off the companys technology. Watson could emerge as a leading AI asset in coming years.

Source: Pe3k / Shutterstock.com

Like IBM,Nvidia (NASDAQ:NVDA) has had a rather forgettable past 12 months. NVDA stock peaked at $290 a share last year, and is almost a full $100 below that now, even after the recent rally. Investors havent necessarily made a mistake either. Nvidia has faced plenty of pressure between the fall in crypto-related demand, the trade war and a general glut of inventory in some of its main product lines.

This severe-but-temporary weakness in main business lines is giving investors a chance to get in on Nvidias artificial intelligence story at a reasonable price, however.

Nvidias deep learning AI offers a great deal of promise. For now, it focuses on self-driving vehicles a major strength for Nvidia in general. Over time, however, Nvidia is aiming to create an ecosystem based around Nvidia GPUs where developers can combine many deep learning concepts into their AI solutions that span various industries. Nvidia will need to demonstrate more success and revenues from automotive AI-driven sales first. But if and when it does, expect investors to take notice and bid NVDA stock back up again.

Source: Katherine Welles / Shutterstock.com

Texas Instruments (NASDAQ:TXN) has some similarities with the previous pick, Nvidia. Both are diversified semiconductor companies that are making a big push in the automotive market. Texas Instruments already has a huge position in autos, via its sensors and processing chips that convert real-world conditions into digital data. That position, along with its wide array of analog chips, has given Texas Instruments one of the most profitable and sustainable businesses in the semiconductor space.

In particular, for those looking for AI stocks, consider Texas Instruments. Its Sitara family of ARM processors enable deep learning. Sitaras have been on the market for a few years now and clients have already incorporated them into applications ranging from smart snow goggles to video players, and Lego programmable robots. Whats most exciting is Texas Instruments place in Googles Nest, a smart thermostat capable of becoming more aware over time as it acquires more data.

Even the giants stumble, however. TXN stock fell sharply following Tuesdays earnings report where the company low-balled forward guidance. That gives investors with a long-term horizon an opportunity. Following the earnings decline, TXN stock is selling at just 24x forward earnings. Thats the cheapest its shares have been in awhile and makes for a nice entry point.

Source: Piotr Swat / Shutterstock.com

CrowdStrike (NASDAQ:CRWD) has had an eventful few months. The company completed its initial public offering in June, and CRWD stock started trading around $60. Traders soon bid it up to $100 on the back of strong earnings and jaw-dropping revenue growth rates.

Things went south in a hurry though. People have been dumping SaaS stocks since September, and Crowdstrike has gotten hit hard. Some analysts are worried about political concerns as well. CrowdStrike helped provide security services to the Democratic National Committee following its 2016 hack. The led to President Donald Trump calling out CrowdStrike during his call with Ukraine. CrowdStrikes CEO said it was a surprise that the company has been dragged into the political circus and that the firm is only focused on customers security.

Regardless, all this noise has created a strong opportunity to pick up a leading AI stock on a steep discount. While plenty of companies do cybersecurity, CrowdStrike has an innovative approach. Its software uses deep machine learning to not only identify threats, but also learn from each attempted attack. Over time, CrowdStrikes AI evolves and becomes more sophisticated as it encounters more and more hacking attempts. Combine that with multiple endpoint security CrowdStrike can defend a user across all their devices and the company has a large and growing lead against other cybersecurity firms. With CRWD stock down nearly 50% since its recent highs, this AI stock is on sale.

Source: Peteri / Shutterstock.com

Not surprisingly, the other huge cloud hosting company is also a leading AI play. Thats right, Microsoft (NASDAQ:MSFT) has a major position in the future of artificial intelligence as well. While Microsoft has missed other major computing trends in the past, under current management, it seems to be much more adept at seeing the future rather than reacting as change happens.

Whats that mean for Microsoft and AI specifically? Good question. Microsoft has aimed to give its customers the first access to human-equivalent level intelligence across a growing number of fields. Microsoft claims to have achieved this sort of AI parity in machine language translation, object detection and speech recognition. Over time, Microsoft expects to be able to broaden its reach to more and more crucial fields.

Of course, rivals like Google would dispute Microsofts leadership in these capabilities. However, Microsoft has a huge advantage over its competition due to Azure. It has access to more data than anyone other than, arguably, Amazon (NASDAQ:AMZN). This gives Microsofts scientists a huge treasure trove of information to process. Its not hard to imagine Microsofts AI becoming the dominant player in coming years.

Source: Jonathan Weiss / Shutterstock.com

A credit card issuer probably isnt the first place youd go looking for artificial intelligence investments. Make no mistake, though, there will be big winners from smart AI applications outside of tech companies. Look no further than Discover Financial Services (NYSE:DFS).

Earlier this year, Discover announced that it has partnered withZest AI (formerly known as ZestFinance) to create a leading credit scoring platform. Through AI the companies are creating a system that will automatically score consumers by credit riskiness and learn from the models underwriting successes and failures over time. Using a growing pile of historical data, a sophisticated AI algorithm should be able to find certain patterns and trends that humans have so far overlooked.

Discovers CEO minced no words in explaining why the company is making such a huge push into AI credit modeling. He said, Banks that fail to invest in machine learning will end up fundamentally uncompetitive in a couple of years. For investors in DFS stock, you could be surprised by the benefits as Discover pulls ahead of less technologically savvy rivals. At less than 9x forward earnings, the price is certainly right to invest in this financial firm.

At the time of this writing, Ian Bezek owned IBM, TXN, CRWD and DFS stock. You can reach him on Twitter at @irbezek.

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7 AI Stocks to Buy to Profit from the Recent Tech Correction - Investorplace.com

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DARPA is betting on AI to bring the next generation of wireless devices online – MIT Technology Review

Its so seamless you almost never notice it, but wireless communication is the foundation upon which much of modern life is built: it powers our ability to text and make calls, hail an Uber, and stream Netflix shows. With the introduction of 5G, it also promises to lower the barrier to safer self-driving cars and kick off a revolution in the internet of things. But this next leap in wireless technology will not be possible without a key ingredient: artificial intelligence.

On Wednesday, 10 teams from industry and academia competed to fundamentally change how wireless communication systems will function. The event was the sixth and final elimination round of the Spectrum Collaboration Challenge (SC2), the latest in a long line of DARPA grand challenges that have spurred development in emerging areas like self-driving cars, advanced robotics, and autonomous cybersecurity.

The challenge was prompted by the concern that the growing use of wireless technologies risks overcrowding the airwaves our devices use to talk to one another.

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Traditionally, so-called radio spectrum hasnt been allocated in the most efficient way. In the US, government agencies divvy it up into mutually exclusive frequency bands. The bands are then parceled out to different commercial and government entities for their exclusive use. While the process helps services avoid interference with one another, whoever holds the rights to a bit of spectrum rarely uses all of it 100% of the time. As a result, a large fraction of the allocated frequencies end up unused at any given moment.

The demand for spectrum has grown to the point that the wastefulness of this arrangement is becoming untenable. Spectrum is not only shared by commercial services; it also supports government and military communication channels that are critical for conducting missions and training operations. The advent of 5G networking only ups the urgency.

To tackle this challenge, DARPA asked engineers and researchers to design a new type of communication device that doesnt broadcast on the same frequency every time. Instead, it uses a machine-learning algorithm to find the frequencies that are immediately available, and different devices algorithms work together to optimize spectrum use. Rather than being distributed permanently to single, exclusive owners, spectrum is allocated dynamically and automatically in real time.

We need to put the world of spectrum management onto a different technological base, says Paul Tilghman, a program manager at DARPA, and really move from a system today that is largely managed by people with pen and paper to a system thats largely managed by machines autonomouslyat machine time scales.

Over 30 teams answered the challenge at the outset of SC2 and competed over three years on increasingly harder goals. In the first phase, teams were asked to build a radio from scratch. In the second phase, they had to make their radio collaborative, so it could share information with other radio systems. In the last phase, the teams had to incorporate machine learning to make their collaborative radios autonomous.

Wednesday night, the 10 finalists went head to head in five simulated scenarios that included supporting communications for a military mission, an emergency response, and a jam-packed concert venue. Each scenario tested different characteristics, such as the reliability of the systems service, its ability to prioritize different types of wireless traffic, and its ability to handle highly congested environments. At the end of the event, a team from the University of Florida took home the $2 million grand prize.

Right now, the winning teams prototype is still in the very early stages and will take a while before it makes its way into our phones. DARPA hopes that SC2 will inspire increased investment and effort in continuing to refine the technology.

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DARPA is betting on AI to bring the next generation of wireless devices online - MIT Technology Review

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US prisons and jails using AI to mass-monitor millions of inmate calls – ABC News

New technology driven by artificial intelligence (AI) is helping prison wardens and sheriffs around the country crack unsolved crimes and thwart everything from violence and drug smuggling to attempted suicides -- in near real time, in some cases -- through digitally mass-monitoring millions of phone calls inside the nations sprawling prison and jail systems.

Despite legally-mandated warnings preceding every prison phone call that the conversation is being recorded and monitored, inmates still regularly reveal astonishing amounts of incriminating information, according to technology company records provided to ABC News and interviews with law enforcement and corrections officials using the systems in multiple states.

In a jail phone call recorded earlier this month in Suffolk County, New York, an inmate allegedly threatened to kill the judge identified by name in the call -- and the prosecutor in his case, as well as the inmates own family.

If I got to stay longer than November Im killing them all when I get out and I mean it! the inmate said on the call, according to a partial transcript provided to ABC News by officials with LEO Technologies, a Los Angeles-based technology firm which began marketing the AI services to U.S. prisons and jails company in 2017. Suffolk officials said the investigation is ongoing.

In another recently-identified conversation, an inmate was recorded on a video call with a 16-year old girl who had been suspended from school, according to officials with the technology company. The student said she was going to return to the school to even the score.

When the video call was flagged and reviewed, investigators determined that while making the threat she was holding a Glock [9 mm pistol] in her hand, said LEO Technologies CEO Scott Kernan, a former secretary of the California Department of Corrections and Rehabilitation. Kernan declined to identify the region where the incident took place, citing an ongoing investigation.

In Alabama, corrections officials said they caught an inmate teaching his wife how to smuggle Suboxone, which aids in opiate withdrawal, into the jail -- by dissolving the drug in water and then using a makeup brush to paint the drug onto the back of postcards that would be mailed to the jail. Another recently-uncovered alleged plot involved smuggling narcotics into a facility by secreting them inside the soles of orthopedic shoes being delivered to an inmate who had been authorized to wear them by a county doctor, according to LEO internal reports.

Data-mining prison phone calls

The AI systems employed in these cases use speech-recognition technology, semantic analytics and machine learning software to build growing databases of searchable words part of a global revolution in neural networks that can understand speech and build databases that just a few years ago were prohibitively difficult to search with AI in real time.

LEO Technologies system is powered by Amazon transcription technology, part of a suite of technology services the retail giant has been marketing to U.S. law enforcement in recent years -- including facial recognition software. Some critics -- including some of Amazon's own employees -- say Amazon is essentially expanding the governments surveillance capabilities for its own profit.

In February, Amazon acknowledged the criticism of its facial recognition software in a statement which offered "proposed guidelines for the proper use of the technology."

"In the two-plus years weve been offering Amazon Rekognition, we have not received a single report of misuse by law enforcement," the statement noted. .

The $1.2 billion prison phone call industry has also come under fire in recent years for charging exorbitant rates for calls, according to annual studies by the Prison Policy Initiative, a non-profit think tank as well as for tracking the locations of individuals called by imprisoned inmates, before the U.S. Supreme Court ruled that practice illegal. One of the nations largest prison phone company has also spurred controversy by recording privileged attorney-client phone calls, sparking litigation that remains ongoing.

But the underlying suite of AI-driven technologies being marketed to U.S. prisons and jails, which rapidly mine existing inmate recorded call databases, is beginning to change the way prisons monitor their inmate populations. The "smart prisons" industry is still evolving. The nation's two largest phone service providers to prisons and jails -- GTL and Securus, its main rival, are building out their own call analytics technologies. Securus is developing biometric voice identification technology and GTL is marketing iPad-like tablets for inmates. LEO -- which contracts with GTL -- is currently operating in five U.S. states, company officials said.

LEO Technologies embeds its own investigators into the corrections and law enforcement agencies it contracts with, and those investigators seed the databases with keywords, phrases and prison slang specific to the region of the country. They then notify law enforcement partners when the system picks up suspicious language or phrasings, a rapid-response process that company officials claim has prevented dozens of attempted suicides over the past two years in several states -- after officials were able to get an inmate psychological counseling in the minutes and hours after the inmate was recorded making references to self-harm. The same process is followed when recorded conversations point to imminent threats of violence within the facility, or to plots to smuggle in contraband.

The system's cost can vary, LEO Technologies officials said, but generally cost between $500,000 and $600,000 a year for a corrections facility housing about 1,000 inmates.

Unmonitored inmate communication ranked third in the list of most pressing priorities in a National Institute of Justice study of the nations nearly 7,000 correctional institutions published this year by the RAND Corporation, coming in behind only controlling prison contraband markets (No. 1) and inmate attacks on infrastructure security systems (No. 2).

Prisons and jails are looking at this very seriously, and several are using it -- trying to figure out the right balance and mix of human and technology, said Jonathan Thompson, executive director of the National Sheriffs Association.

"Voice recognition has been around for 12 or 13 years," he said. "The question is: what do you do with the data?"

The technology has the potential to solve a decades-old challenge inside the nations corrections facilities.

One of the biggest operational issues that has plagued this industry of automated inmate telephone recording has been the lack of staffing to monitor every single call, said John Shaffer, a corrections technology expert and one of the authors of the NIJ study who spent 31 years working in Pennsylvania state and county prisons. And, frankly, most inmate calls are innocuous.

Retired warden Robert Hood, who from 2002 to 2005 ran Colorados ADX Florence Supermax federal facility, agreed.

In the three prisons where I was warden, it was pretty much reactive: a very small percentage [of inmate calls] was monitored and it was done after the fact, when there was a crime or an alleged crime, that youd get on to your phone monitoring equipment and try and hunt down what occurred, he said.

Shaffer said that U.S. prison wardens have tried a variety of ways to balance the regular, proactive monitoring of inmate calls with limited staff resources.

There [have] been different approaches over the years, he said. I remember Walla Walla penitentiary out in the state of Washington used to have officers sitting there in the towers at night listening to inmate calls for hours and hours and hours -- and that was their solution. And it never really worked as an operational solution.

Briefed on the technology, Hood said he recognized its potential value for intelligence, as long as it was used in conjunction with, and did not supplant, traditional corrections practices like working with confidential informants, daily engagement with inmates, inspections, and drug-sniffing dog patrols.

Heres where my mind goes, Hood said. Would Whitey still be alive today if we had that intelligence? he wondered, referring to the vicious murder of the Boston organized crime boss and FBI informant James "Whitey" Bulger last fall, the day after he was transferred to the Hazelton federal penitentiary in West Virginia. Bulger was beaten with sock-wrapped padlocks and his eyes were nearly gouged out with a shiv, or makeshift knife.

Would there be some talk back and forth about hitting Whitey when he went to West Virginia to the federal prison?" Hood wondered. "And would [pedophile financier Jeffrey] Epstein -- who committed suicide, allegedly -- would there have been any value in the artificial intelligence in those types of scenarios? I want to believe yes, there would be value. The system that we had didnt work.

Cracking codes

Inside prisons and jails, criminal conduct is conveyed in code, where words can have very different meanings in different places, and can change from month to month or year to year. An AK-47, commonly known in Alabama prison culture as a stick, is called a tube elsewhere, or a mower, or a chopper, according to corrections officials in two states.

"Synthetic marijuana -- which is sometimes called 'spice' -- theyve taken to calling it no show because if you smoke synthetic marijuana, it's not going to show up on the drug test," said David Thompson, a recently-retired commander in the Jefferson County Sheriffs Office, who is not related to Jonathan Thompson.

Methamphetamine is known in the South as mylar or ice, while its commonly referred to in Los Angeles as glass, or window, officials said.

A binky is a homemade syringe for injecting drugs, often made out of an eyedropper, a pen shaft, and a guitar string, according to "Prison Diaries," a popular blog about prison life authored by a former inmate, which originally appeared in the New Haven Independent newspaper.

A year from now, all that slang could be obsolete so investigators are constantly feeding new intelligence about prison slang into databases tailored to their unique jurisdiction or regional area.

Weve taught the system how to speak inmate, said James Sexton, another executive at LEO Technologies.

At other times, the intentions an inmate conveys in a prison phone call are crystal clear.

After a Birmingham, Alabama man was arrested in 2017 on outstanding felony warrants and his car was towed to a local impound yard, law enforcement officials said they were tipped by LEOs technology to search his car for drugs.

He calls family members and asks them to go to the tow yard, and get a locked camera case from his car, said David Thompson. And he gives them the three-digit code to unlock the case.

Law enforcement officers alerted to the phone call got a search warrant and rushed to the impound yard with drug-sniffing dog.

We sent our narcotics people to the tow yard and beat the family there, Thompson said. We didnt even have to break the case open because we had the code.

Inside the camera case, authorities found three grams of cocaine, two grams of heroin and an undetermined amount of the sedative alprazolam, Thompson said.

In another recent incident in Jefferson County, authorities said they caught an inmate running a prostitution ring from inside the prison.

He was complaining [on a prison phone] to the handler that a human trafficking victim was too far away for him to control her, David Thompson said. He was complaining that she was in California. He wanted her brought back closer to [Alabama], Thompson said. When we showed up at the hotel in Birmingham where the inmate had sent the trafficking victim, we were able to intercept them at the hotel and charge the handler.

He was running this prostitution ring and he had never left the building, Thompson said.

Thwarting suicides and sharing intelligence

The new technology is also thwarting planned or attempted suicides, corrections officials in three states said.

"You always have reservations when youre talking about new technology, said Oxford, Alabama police chief Bill Partridge. But it made a believer out of me in the first week. Weve solved homicides, arsons, stopped jail contraband, solved some cold cases. But to me, though, the suicide prevention is the most valuable tool.

Its just tragic all around when someone wants to take their life," Partridge said. "But taking your life in a penal institution -- thats a huge news story, because were there to maintain their health and well-being. It [the AI technology] saves taxpayers copious amounts of money and it also helps the family because they dont have to deal with that situation.

Traditionally, U.S. law enforcement and intelligence agencies have resisted sharing information in order to protect their investigations. From small town detectives all the way up to the rivalry exposed after the 9/11 attacks between the Federal Bureau of Investigation (FBI) and the Central Intelligence Agency (CIA), investigators are notorious for aggressively protecting information they develop.

But the new A.I. technologies may be starting to change all that.

When theres a crime on the outside that occurs, were pretty much the first ones they call, said Suffolk County Undersheriff Kevin Catalina, a former gang investigator in the New York City Police Department (NYPD) Intelligence Division.

For instance, after a series of shootings in Bellport, corrections officials were able to quickly produce intelligence that the shootings were connected.

When we were able to pass that information along to our partners on the outside, they were amazed -- first of all that this existed within the jail and [secondly] that we were able to extract it and get it back out so fast, Catalina said. As a result of that, that kind of reverberated throughout the Suffolk County Police Department, and now were getting more and more inquiries from detectives regarding crimes on the outside.

"The turnaround is so quick, its like a different level of respect," said Suffolk County Sheriff's Office Sergeant Joseph Nasta.

Marrying old school tactics to new technology

Still, veteran corrections officials are skeptical than even sophisticated technologies like AI will permanently change the culture inside prisons and jails.

You hope that the technology doesnt make the people lazy, make them less effective at walking the ranges, talking to the inmates, getting that feeling, you know? Hood told ABC News. Its hard to explain. When you walk into a prison in the morning, most senior wardens know if theres something going to jump off, whether theres something going on, just because of the expressions on some of the inmates faces, the confidential informants, you could just feel it in the air."

Shaffer said that over the years he has developed what he described as a profound respect for the imaginative ways in which prisoners subvert rules inside correctional facilities.

Its a constant game of cat and mouse as we figure out an intervention, inmates figure out a circumvention. Certainly its getting better. Its better than it ever has been.

One ongoing technological concern is contraband cell phones.

One of the things driving the market in [contraband] cell phones is to get away from being monitored and recorded, Shaffer said. And the cost of a contraband cell has escalated in prison and it used to be astronomical."

The U.S. Department of Justice has been testing out technology for years that would detect the use of contraband cell phones insides corrections facilities, but has yet to find a cost-effective method that doesn't lead to unintended consequences, like jamming the cell phones of civilians in and around urban facilities.

Prisoners' rights activists point out that the AI technology to mass-monitor phone calls is being used in not just prisons but jails, where many of the inmates have been charged but not convicted of a crime, and may not be able to afford bail.

"I can already envision whats about to happen," said Bianca Tylek, executive director of Worth Rises, an inmate advocacy group that's critical of the commercialization of the corrections industry. "LEO creates a partnership [with the telephone providers], and passes on a cost to the inmate. They will be using surveillance to help with prosecutions, investigations and law enforcement. But the majority of people in jail are pretrial, and are subjected to additional levels of surveillance because they cant afford bail -- and that really seems unfair and subjective. Rich people can get bail and not be subject to this added level of surveillance."

Hood, the former supermax warden, said he remains wary of the relentless march of surveillance technology.

"Thats great stuff," he said in a phone interview. "But I do fear a little bit about how much intelligence will [eventually] get outside the prison -- where youre starting to look at conversations like the one were having right now."

ABC News' Josh Margolin contributed to this report.

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US prisons and jails using AI to mass-monitor millions of inmate calls - ABC News

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Human Compatible by Stuart Russell review AI and our future – The Guardian

Heres a question scientists might ask more often: what if we succeed? That is, how will the world change if we achieve what were striving for? Tucked away in offices and labs, researchers can develop tunnel vision, the rosiest of outlooks for their creations. The unintended consequences and shoddy misuses become afterthoughts messes for society to clean up later.

Today those messes spread far and wide: global heating, air pollution, plastics in the oceans, nuclear waste and babies with badly rewritten DNA. All are products of neat technologies that solve old problems by creating new ones. In the inevitable race to be first to invent, the downsides are dismissed, unexplored or glossed over.

In 1995, Stuart Russell wrote the book on AI. Co-authored with Peter Norvig, Artificial Intelligence: A Modern Approach became one of the most popular course texts in the world (Norvig worked for Nasa; in 2001, he joined Google). In the final pages of the last chapter, the authors posed the question themselves: what if we succeed? Their answer was hardly a ringing endorsement. The trends seem not to be too terribly negative, they offered. A lot has happened since: Google and Facebook for starters.

In Human Compatible, Russell returns to the question and this time does not hold back. The result is surely the most important book on AI this year. Perhaps, as Richard Brautigans poem has it, life is good when we are all watched over by machines of loving grace. But Russell, a professor at the University of California, Berkeley, sees darker eventualities. Creating machines that surpass our intelligence would be the biggest event in human history. It may also be the last, he warns. Here he makes the convincing case that how we choose to control AI is possibly the most important question facing humanity.

Russell has picked his moment well. Tens of thousands of the worlds brightest minds are now building AIs. Most work on one-trick ponies the narrow AIs that process speech, translate languages, spot people in crowds, diagnose diseases, or whip people at games from Go to Starcraft II. But these are a far cry from the fields ultimate goal: general purpose AIs that match, or surpass, the broad-based brainpower of humans.

Its is not a ludicrous ambition. From the start, DeepMind, the AI group owned by Alphabet, Googles parent company, set out to solve intelligence and then use that to solve everything else. In July, Microsoft signed a $1bn contract with OpenAI, a US outfit, to build an AI that mimics the human brain. It is a high stakes race. As Vladimir Putin said: whoever becomes the leader in AI will become the ruler of the world.

Russell doesnt claim we are nearly there. In one section he sets out the formidable problems computer engineers face in creating human-level AI. Machines must know how to turn words into coherent, reliable knowledge; they must learn how to discover new actions and order them appropriately (boil the kettle, grab a mug, toss in a teabag). And like us, they must manage their cognitive resources so they can reach good decisions fast. These are not the only hurdles, but they give a flavour of the task ahead. Russell suspects it will keep researchers busy for another 80 years, but stresses the timing is impossible to predict.

Even with apocalypse camped on the horizon, this is a wry and witty tour of intelligence and where it may take us. And where exactly is that? A machine that masters all the above would be a formidable decision maker in the real world, Russell says. It would absorb vast amounts of information from the internet, TV, radio, satellites and CCTV and with it gain a more sophisticated understanding of the world and its inhabitants than any human could ever hope for.

What could possibly go right? In education, AI tutors would maximise the potential of every child. They would master the vast complexity of the human body, letting us banish disease. As digital personal assistants they would put Siri and Alexa to shame: You would, in effect, have a high-powered lawyer, accountant, and political advisor on call at any time.

And what of the downsides? Without serious progress on AI safety and regulation, Russell foresees messes aplenty and his chapter on misuses of AI is grim reading. Advanced AI would hand governments such extraordinary powers of surveillance, persuasion and control that the Stasi will look like amateurs. And while Terminator-style killer robots are not about to eradicate humanity, drones that select and kill individuals based on their faceprints, skin colour or uniforms are entirely feasible. As for jobs, we may no longer make a living by providing physical or mental labour, but we can still supply our humanity. Russell notes: We will need to become good at being human.

Whats worse than a society-destroying AI? A society-destroying AI that wont switch off. Its a terrifying, seemingly absurd prospect that Russell devotes much time to. The idea is that smart machines will suss out, as per HAL in 2001: A Space Odyssey, that goals are hard to achieve if someone pulls the plug. Give a superintelligent AI a clear task to make the coffee, say and its first move will be to disable its off switch. The answer, Russell argues, lies in a radical new approach where AIs have some doubt about their goals, and so will never object to being shut down. He moves on to advocate provably beneficial AI, whose algorithms are mathematically proven to benefit their human users. Suffice to say this is a work in progress. How will my AI deal with yours?

Lets be clear: there are plenty of AI researchers who ridicule such fears. After the philosopher Nick Bostrom highlighted potential dangers of general purpose AI in Superintelligence (2014), a US thinktank, the Information Technology and Innovation Foundation, gave its Luddism award to alarmists touting an artificial intelligence apocalypse. This was indicative of the dismal debate around AI safety, which is on the brink of descending into tribalism. The danger that comes across here is less an abrupt destruction of the species, more an inexorable enfeeblement: a loss of striving and understanding, which erodes the foundations of civilisation and leaves us passengers in a cruise ship run by machines, on a cruise that goes on forever.

Human Compatible is published by Allen Lane (25). To order a copy go to guardianbookshop.com or call 020-3176 3837. Free UK p&p over 15, online orders only. Phone orders min p&p of 1.99.

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Human Compatible by Stuart Russell review AI and our future - The Guardian

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Why Micron is Getting into the AI Accelerator Business – The Next Platform

Micron has a habit of building interesting research prototypes that offer a vague hope of commercialization for the sheer purpose of learning how to make its own memory and storage subsystem approaches more tuned to next generation applications.

We saw this a few years ago with the Automata processor, which was a neuromorphic inspired bit of hardware that focused on large-scale pattern recognition. That project has since folded internally and moved into a privately funded effort from a startup aiming to make it market ready, which is to say that it has all but disappeared from view since that was a couple of years ago.

There is more here for anyone interested in the Automata architecture, but for those curious about why Micron wants to get into the accelerator business with one-off silicon projects like that or its newly announced deep learning accelerator (DLA) for inference, its far less about commercial success than it is learning how to tune memory and storage systems for AI on custom accelerators. In fact, the market viability of such a chip would be a delightful bonus since the real value is getting a firsthand understanding of what deep learning applications need out of memory and storage subsystems.

This deep learning accelerator might be counted among those on the market (and thats a list too long to keep these days) but we do not expect the company to make a concentrated push to go after a large share. This is for the same reasons we dont expect much to emerge into IBMs product line from its research divisions. They are all efforts to build better mainstream products. If there is commercial gain, great, but it is not the wellspring of motivation.

Nonetheless, it is worth taking a quick look at what Micron has done with its inference accelerator since it could set the tone for what we may see in other products functionally, especially for inference at the edge.

Last year, Micron bought a small FPGA-based startup that spun out of Purdue University called FWDNXT (as in Forward Next). It also acquired FPGA startup, Pico Computing, in 2015 and has since been hard at work looking for where reprogrammable devices will fit for future applications and what to bake into memory to make those perform better and more efficiently.

The FWDNXT technology is at the heart of Microns new FPGA based deep learning accelerator, which gets some added internal expertise from Micron via the Pico assets. The architecture is similar to what weve seen in the market over the last few years for AI. A sea of multiply/accumulate units geared toward matrix vector multiply and the ability to do some of the key non-linear transfer functions. Micron took the FWDNXT platform against some tough problems and worked to do things like build tensor primitives inside the memory (so instead of floating point based scatter gather they could go fetch a matrix sitting in a buffer versus going over memory) They have also used the platform to build a software framework that is hands-off from an FPGA programming perspective (just specifying the neural network).

Micron wants to target energy efficiency by going to the heart of the problemdata movement with the performance goal of better memory bandwidth. All of this creates an accelerator that can be useful, but Micron was better able to see how to create future memory by working with FWDNXT to get the device ready.

It became obvious that if we are tasked with building optimized memory and storage we need to come up with what is optimal rather than just throwing in a bag of chips and hoping it works, explains Steve Pawlowski, VP of Advanced Technology at Micron. We are learning about what need to do in our memory and storage to make them a fit for the kinds of hard problems in neural networks we see ahead, especially at the edge.

Pawlowski is one of the leads behind some of Microns most notable efforts in creating specialized or novel architectures like Automata. He previously led architectural research initiatives at Intel where part of his job was to look how prototype chips were solving emerging problems in interesting ways and if those architectures held promise or competitive value. In the process, he developed an eye for building out new programs at Micron that took a research concept and tested its viability and role in using or improving memory devices.

By not having observability into the various networks on the compute side we could only guess if the things we were building into memory would be useful, Pawlowski says. The only way we could get real observability into how neural networks area executing was to have the entire pipeline so we could go in and instrument every piece of it. This is how we end up making better memory.

He adds that they build this base of knowledge by looking at some of the most complex problems and architecting from there, including with a cancer center that is doing disease detection at scale. Here accuracy is the biggest challenge. Theyve also been working with a very large high-energy physics entity (venture a guess) where the drivers are performance and latency. By taking a view of solving problems with different optimization points (accuracy versus raw performance) Micron is hoping to strike a balance that can inform next generation memory.

During these research and productization experiments, Micron does get a forward look at what future memory might need for a rapidly evolving set of workloads like AI.

The funny thing is, what theyre learning is the inherent value of what they already built as a commercial product several years agosomething that had great potential but strong competition. That would be hybrid memory cube (HMC) which has since been folded as a product as Micron focuses on what is next for that concept of memory stacked on top of logic.

As Micron looks at AI workloads the potential for this exact thing, which exists in plenty of devices now as rival HBM, has even more potential, even for inference. It might sound heavy-handed for an energy-efficiency-focused set of workloads, but more demands from the inference side will mean greater compute requirements. Doing all of that in a stacked memory device at the edge might seem like an expensive stretch, but Pawlowski says this is what he sees in his crystal ball.

There may be a renaissance of memory stacked on logic doing neural networks at the edge. The need for higher memory bandwidth will matter more in the years ahead. There will also be a need to reduce memory interconnect power too, Pawloski says, adding, I believe there will come a day when an architecture that is in the HMC style will be the right thing. By then it might not just be a memory device, it could be an accelerator. There will be other capabilities that come along there as well, including better ECC, for instance.

Its hard to tell where the research ends and the commercial potential begins with some of Microns research efforts for new chips or accelerators. If indeed these flow into the next instantiation of HMC, whatever that might be, this is interesting backstory. But when it comes to innovating in memory in a meaningful way that captures what AI chips need now the market might move on before Micron has a chance to intercept it with who knows what analog and other inference devices at the fore.

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AI allows paralyzed person to ‘handwrite’ with his mind – Science Magazine

By Kelly ServickOct. 23, 2019 , 12:05 PM

CHICAGO, ILLINOISBy harnessing the power of imagination, researchers have nearly doubled the speed at which completely paralyzed patients may be able to communicate with the outside world.

People who are locked infully paralyzed by stroke or neurological diseasehave trouble trying to communicate even a single sentence. Electrodes implanted in a part of the brain involved in motion have allowed some paralyzed patients to move a cursor and select onscreen letters with their thoughts. Users have typed up to 39 characters per minute, but thats still about three times slower than natural handwriting.

In the new experiments, a volunteer paralyzed from the neck down instead imagined moving hisarm to write each letter of the alphabet. That brain activity helped train a computer model known as a neural network to interpret the commands, tracing the intended trajectory of his imagined pen tip to create letters (above).

Eventually, the computer could read out the volunteers imagined sentences with roughly 95% accuracy at a speed of about 66 characters per minute, the team reported here this week at the annual meeting of the Society for Neuroscience.

The researchers expect the speed to increase with more practice. As they refine the technology, they will also use their neural recordings to better understand how the brain plans and orchestrates fine motor movements.

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AI allows paralyzed person to 'handwrite' with his mind - Science Magazine

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Ultria Unveils Orbit AI, the Latest Addition to Its AI Powered CLM Capabilities – The Trentonian

PRINCETON, N.J., Oct. 25, 2019 /PRNewswire/ -- Ultria, a leading provider of Enterprise Contract Lifecycle Management in the cloud, unveiled ULTRIA ORBIT AI, a next-generation, Artificial Intelligence solution, expanding beyond the AI-powered Contract Management portfolio.

"Orbit AI is a complementary solution for our contract management application,"Arthur Raguette, Executive Vice President, Ultria said. "We are proud to introduce Orbit as a part of our contract management suite and to provide additional value to our customer's CLM experience. We expect Orbit to be a game-changer and take contract management across the horizon."

Ultria Orbit is designed to assist users across the contract lifecycle, from drafting and assembling contracts, streamlining work flows, to post-contract compliance and obligations management. Orbit's abilities to extract metadata and identify clause match conditions are powered by proprietary Artificial Intelligence algorithms.

Orbit embodies three different personas, a Guide, an Explorer, and a Protector, all powered by Ultria's Artificial Intelligence capabilities to streamline your contract management journey.

Orbit Guidesteers through key stages of intake request, authoring, assembling, and negotiating.

Orbit Explorerworks as an intelligent companion for more intuitive searching with more accurate results and faster reporting and analytics.

Orbit Protectorsafeguards organizations from the complexities of a rapidly shifting regulatory landscape, ever-increasing commercial compliance risks and post-award commercial and compliance management.

Read more about Ultria Orbit hereand harnessOrbit's AI capabilities to enhance your team's performance and transform your contracts into dynamic, intuitive, and smart legal documents for informed decision making.

About Ultria:

Ultria develops and licenses Artificial Intelligence powered applications, including its flagshipContract Lifecycle Management solutionfor the enterprise. Ultria CLM is a proven, scalable, SaaS-deployed Contract Lifecycle Management system that leverages Artificial Intelligence and Machine Learning to be robustly and rapidly provisioned in today's complex business landscapes. Ultria Orbit, Ultria's Artificial Intelligence enables teams to analyze contract terms and extract metadata from any contract. For information on Ultria, visitwww.ultria.com.

Press ContactRewaKulkarni, Marketing and Public Relations, UltriaEmail: rewa.kulkarni@ultria.com

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