Daily Archives: June 23, 2017

Researchers in Europe are developing a camera that will literally have a mind of its own, with brainlike algorithms that process images and light sensors that mimic the human retina. Its makers hope it will prove that artificial intelligencewhich today requires large, sophisticated computerscan soon be packed into small consumer electronics. But as much as an AI camera would make a nifty smartphone feature, the technologys biggest impact may actually be speeding up the way self-driving cars and autonomous flying drones sense and react to their surroundings.

The conventional digital cameras used in self-driving and computer-assisted cars and drones as well as in surveillance devices capture a lot of extraneous information that eats up precious memory space and battery life. Much of that data is repetitive because the scene the camera is watching does not change much from frame to frame. The new AI camera, called an ultralow-power event-based camera, or ULPEC, will have pixel sensors that come to life only when the camera is ready to record a new image or event. That memory- and power-saving feature will not slow performancethe camera will also have new electrical components that allow it to react to changing light or movement in a scene within microseconds (millionths of a second), compared with milliseconds (thousandths) in todays digital cameras, says Ryad Benosman, a professor at the University Pierre and Marie Curie who leads the Vision and Natural Computation group at the Paris-based Vision Institute. It records only when the light striking the pixel sensors crosses a preset threshold amount, says Benosman, whose team is developing the learning algorithms for an artificial neural network that serves as the cameras brain. An artificial neural network is a group of interconnected computers configured to work like a system of flesh-and-blood neurons in the human brain. The interconnections among the computers enable the network to find patterns in data fed into the system, and to filter out extraneous information via a process called machine learning. Such a network does away with not only acquiring but also processing irrelevant information, thus making the camera faster and requiring lower power for computation, Benosman says.

The AI camera's photo sensorsits eyeswill consist of tiny pieces of semiconductors and circuitry on silicon, which turn changes in light into electrical signals sent to the neural network. Integrated circuits and a new type of electronic component called a memory resistor or memristor, acting as the equivalent of synaptic connections, will process the information in those signals, says Sren Boyn, a researcher at the Zurich-based Swiss Federal Institute of Technology who worked with the CNRS-Thales joint research unit that is now working with Benosmans team. One of the biggest challenges to that approach is that memristor technologyfirst theorized in 1971 by University of California, Berkeley, professor emeritus Leon Chua (pdf) and later mathematically modeled by HewlettPackard Labs researchers in 2008is still largely in the development stage, which would explain why for the ULPEC project is not expected to have a working device until 2020.

The AI cameras memristors will consist of a thin layer of a ferroelectric materialbismuth ferritesandwiched between two electrodes, says Vincent Garcia, a research scientist at French scientific research agency CNRS/Thales, which is developing the ULPEC memristor. Ferroelectric materials have positive and negative sidesbut applying voltage reverses those charges. Thus, the resistance of memristors can be tuned using voltage, Garcia explains. Similar to our brains learning ability that is dependent on the stimulation of synapses, which serve as connections between our neurons, this tunable resistance helps in making the network learn. The more the synapse is stimulated, the more the connection is reinforced and learning improved.

The combination of bio-inspired optical sensors and neural networks will make the camera an especially good fit for self-driving cars and autonomous drones, says Christoph Posch, chief technology officer of the Paris-based start-up Chronocam, which is designing the cameras optical sensors. In self-driving cars the onboard computer must react to changes very quickly while navigating through traffic or determining the movement of pedestrians, Posch explains. The ULPEC can detect and process these changes rapidly. German automotive equipment manufacturer Boschalso involved in the projectwill investigate how the camera might be used as part of its autonomous and computer-aided driving technology.

The researchers plan to place 20,000 memristors on the AI cameras microchip, says Sylvain Saighi, an associate professor of electronics at the University of Bordeaux and head of the $5.57-million ULPEC project.

Getting all of the components of a memristor neural network onto a single microchip would be a big step, says Yoeri van de Burgt, an assistant professor of microsystems at Eindhoven University of Technology in the Netherlands, whose research includes building artificial synapses. Since it is performing the computation locally, it will be more secure and can be dedicated for specific tasks like cameras in drones and self-driving cars, adds van de Burgt, who was not involved in the ULPEC project.

Assuming the researchers can pull it off, such a chip would be useful well beyond smart cameras because it would be able to perform a variety of complicated computations itself, rather than off-loading that work to a supercomputer via the cloud. In this way, Posch says, the camera is an important step toward determining whether the underlying memristors and other technology will work, and how they might be integrated into future consumer devices. The camera, with its innovative sensors and memristor neural network, could demonstrate that AI can be built into a device in order to make it both smart and more energy efficient.

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Quick-Thinking AI Camera Mimics the Human Brain - Scientific American

Predictions for the potential of artificial intelligence wax poetic solutions from climate change to curing disease but the everyday applications make it seem far more mundane, like a glorified clock radio.

Thankfully, the future may be closer than we think. And the miraculous feats are not happening in Silicon Valley X-Labs in a plot twist, nonprofits are leading the charge in creating human-centered applications of the hottest AI technologies. From the simplest automated communications to contextual learnings based on analysis of deep data, these technologies have the potential to rapidly scale and improve the lives of our most underserved communities.

Take chatbots for example, a new spin on mobile messaging that has historically been human-powered. Organizations like TalkingPoints and mRelief have for years used simple mobile messaging to meet users where theyre at. Recently, tech nonprofits are taking a new approach. Raheem.ai, a Facebook Messenger bot for reporting and rating experiences with police officers, engages with users to walk them through reporting police incidents and provide follow-on support. The interactions are simple, but powerful. Do Not Pay, the worlds first robot lawyer, started out as a bot to repeal parking tickets and now helps fight landlords in negligent housing situations, and even helps the homeless find and apply for social services. These chatbots eliminate the friction of traditional reporting and serve as legal empowerment in your pocket.

Crisis Text Line still implements a human-to-human volunteer model, but the tech nonprofit has the largest open source database of youth crisis behavior in the country, and has been able to use AI to dramatically shorten response time for high-risk texters from 120 seconds to 39. Crisis Text Line leveraged machine learning to identify the term ibuprofen as 16 times more likely to predict the need for emergency aid than the word suicide. Now using AI, messages containing the word ibuprofen are prioritized in the queue.

Machine learning even allows you to select the energy source that powers your home appliances. WattTime creates software that enables smart hardware devices to prioritize clean energy with a simple flip of a switch. Their product relies on machine learning to detect when to tell smart devices like thermostats to pull from the power grid, based on surges in clean energy. This means your A/C may turn on five minutes earlier or later than it typically would, because the algorithms instruct your utilities to capitalize upon instances of excess clean energy from sources like windmills, thus minimizing the use of dirty power.

Quill, a free online tool that helps students measurably improve grammar and writing, discovered that natural-language processing was essential to remedy students struggles with sentence fragmentation. Using open source tools and online training programs, Quills technical team built its own fragment detection algorithm powered by a combination of machine learning and natural-language processing. Quills methodology is exemplary for resource-constrained tech nonprofits. It leveraged Wikipedia to amass a dataset of 100,000 high-quality sentences, integrated the natural-language processing tool Spacy.io to break the sentences down, and incorporated Tensorflow for data classification.

The result? Quills fragment-detection algorithm accurately detected sentence fragments 84 percent of the time, and this will only continue to improve. Other tech nonprofits, like Dost Education, forecast using natural-language processing down the line to monitor their impact assessments with teachers and parents.

While many instances of AI pool internally sourced data, data mining allows organizations to execute deep research faster, or to scrape mass information on their target market to make product decisions based on behaviors and trends. The Pulitzer Prize-winning reporting on the Panama Papers conveys the growing importance of data mining in investigative journalism. With 261 gigabytes of data, data mining was essential if the team of 100 journalists were to dig through the largest mass of leaked data in the history of journalism.

Transparency Toolkit, a Berlin-based tech nonprofit, launched its first tool, ICWatch, which implements data mining to scrape information from publicly available profiles and resumes to identify individuals involved in activities ranging from government surveillance to drone strikes. The organization runs several different tools and projects designed to democratize the big data playing field for human rights activists and journalists.

Yes. As the cost of AI implementation drops, it will become ubiquitous across software. The AI use case for a nonprofit is significant because incentives are well aligned to collect and open source the collected data. Effective implementation of AI requires massive data. Profit motives can restrain a companys incentive to open its data, but this is not so for nonprofits. Open data serves the broader purpose of public education and knowledge sharing. As tech nonprofits deploy these technologies and open source their findings, they can deepen the capacity of all AI applications.

As tech nonprofits deploy these technologies and open source their findings, they can deepen the capacity of all AI applications.

However, corporations have a role to play, too. Businesses like Google and Accenture are leveraging their internal AI talent to build tools for positive impact. Google.org is working with Pratham Books StoryWeaver, a platform that connects readers, authors, illustrators and translators to massively expand the number of childrens e-books available in mother tongues. Through an integration with the AI-powered Google Translate API, StoryWeaver is expanding its library to 200,000 titles in 60 languages.

Accenture sees Responsible AI as both an opportunity and a responsibility for business, government and technology leaders to apply the technology in the right way, using human-centric design principles such as accountability, transparency and fairness. Accenture Labs in Bangalore is developing workforce accessibility solutions called Drishti, using Responsible AI to empower the visually impaired, in collaboration with the National Association for the Blind.

The tech for good use cases for AI are endless, ranging from refugee aid, to bankruptcy filings, to predictive solutions in child welfare. We are still in the early days of true implementation of AI, but in the tech nonprofit sector, the future looks bright.

Shannon Farley is the co-founder and executive director of Fast Forward, the first and only accelerator exclusively for tech nonprofits. Through her work at Fast Forward, Farley has accelerated 23 tech nonprofits that are now impacting over 18 million lives around the world. Previously, she was the founding executive director of Spark, the world's largest network of millennial philanthropists; she also co-founded The W. Haywood Burns Institute, a MacArthur Award-winning juvenile-justice reform organization. Reach her @Shannon_Farley.

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Nonprofits, not Silicon Valley startups, are creating AI apps for the greater good - Recode

Despite the tech company takeover of Cannes, the ad industrys current infatuation artificial intelligence confined its appearances to panels and presentations.

But a few AI aspirations (deployments is too strong a word in many cases) are worth calling out.

Tencent

The Chinese maker of the popular WeChat application has a machine learning agenda that rivals Google and IBM.

WeChat wants to build expertise in machine learning across a range of functionalities: advanced speech recognition (understanding spoken language); natural language processing (understanding the context behind unstructured text or speech); and computer vision (facial, image and video recognition). All of these capabilities are built on a machine learning foundation, so the systems are designed to improve with use.

Tencent then hopes to apply this tech to an array of applications, including gaming AI, chatbots, smart assistants, social ads and video searches.

The ambition makes sense: Tencent is more than WeChat. Beyond social networking, its tendrils extend into news content, games and entertainment, a mobile browser, financial services and Wi-Fi services. Its social ads business has also risen dramatically in the past year, a spokesperson said.

Because of all of those different use cases, and all of the data Tencent collects, it needs artificial intelligence. That why two years ago, the companys C-levels decided to invest in AI as a core technology.

Last year, Tencent built an AI lab with 50 scientists doing AI research and 100 engineers who could apply it. The spokesperson said thats actually small compared to similar teams at Google and Chinese search engine Baidu, but its a start.

The lab lets Tencent apply AI technologies quickly across its entire portfolio, and its already pushed image recognition into its QQ app to improve photo transfers.

Eventually, Tencent wants to offer its AI through a platform model so that if another vendor needs an instance of gaming AI or intelligence for a chatbot, it can get the core tech from Tencent. Its very similar to how IBM has been offering Watson through Bluemix.

The additional benefit for Tencent is that in opening up its AI capabilities for other developers, it can collect more data and become more intelligent.

Annalect

Omnicoms data and marketing sciences group, Annalect Utility Bot Interface (AUBI), made its debut at Cannes. During the demo, when it wasnt being undermined by the venues crappy Wi-Fi signal, the bot responded to typed queries about where to find great ros or the nearest party.

That was just to show off to the Cannes crowd. AUBIs real purpose is to empower Omnicoms nondata wonks to use more data. Remember how at last years Cannes, creative agencies like BBDO were starting to get into programmatic, and how BBDO even has a director of data solutions to help creatives and other nonquants use data?

AUBI is another step toward facilitating data use across Omnicoms employees. So, if a creative staffer or a media planner has a question about a certain audiences tendencies, they can punch it into AUBIs text field, and voil: an answer.

At least thats the hope. AUBI hasnt yet been deployed widely within Omnicom, and whether or not its agencies decide to use it will be the real test.

IBM Watson/Weather Channel And Soul Machines

If youve seen Avatar or Rise of the Planet of the Apes, youve seen Dr. Mark Sagars work.

For those films, Sagar won two Oscars for his facial motion capture work. But at a Cannes event hosted by MEC, Sagar was repping his startup Soul Machines, which creates avatars or in his preferred parlance, digital humans to be used as customer service representatives. Watson, of course, provides the AI.

Despite the viability of video conferencing, contact centers still rely on voice calls. But the problem with video conferencing is that it presumes the service rep is well-groomed and camera-ready, and lets be honest thats just not everyones forte.

Sagar insists his digital humans arent meant to replace service reps. Rather, like automated contact centers, they can relieve human employees of more menial tasks.

Digital humans, however, are lifelike and are designed to mimic emotion to establish a human-like connection. Are you calling because your credit card was stolen? The digital human will look sad. Are you ordering flowers to celebrate your 50th wedding anniversary? The digital human will duly look happy.

Sagar said his digital humans are already in a handful of pilots and that the solution is scalable, not particularly cost-prohibitive and highly customizable.

But actual nondigital humans are still involved, at least in the testing phase. For a project Sagar is doing with the Australian government working with people with disabilities, for example, the deployment is highly curated, with psychologists ensuring that the digital humans respond to human users in the best way possible.

Of course, another test will be to see whether digitized faces, despite recent advancements, have fully crossed the uncanny valley at least enough for most consumers to accept.

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AI Had A Modest Showing At Cannes, But Here Are Some Notable Developments - AdExchanger

The venture capital community was shocked last week with Element AIs fundraise. How can a Canadian startup founded in late 2016 raise a Series-A in June 2017 for a cool $102 million?

Microsoft, NVIDIA, and Intel Capital were among the illustrious group of VCs who put in this sum into a round that makes almost all other Series-A rounds look paltry.

When you peruse Elements webpage you see a simple HTML site that talks vaguely about AI-Strategy consulting, expert matching, and AI-as-a-Service (AIaaS). It leaves you scratching your head, wondering how this firms ideas are earth shattering enough for such a rocket trajectory.

Well, heres how: They are brilliant because they hit on the biggest pain for AI in the coming 10 years the need for business executives in low-tech companies to access AI in an easy-to-use way that still gives them customized solutions from tier-1 technologies and the worlds best AI-talent.

A majority of the business world is currently locked out of the AI universe. A typical example is a manufacturing company in the middle of Germany with $50 million revenues lets call it Plant Co. Plant Co would like to increase its production or cost-of-goods sold or decrease maintenance cost for its manufacturing plant, but it doesnt know how to start using AI for it.

You might think that because 800+ AI startups with clever tech exist, there is an abundance of amazing AI tech available for a mid-sized company like Plant Co. Yet even with all of these solutions, there remains a gap. The gap exists both because these startup services may not immediately deliver adequate results and because many of the best technologies are winding up behind closed doors.

Apple, Microsoft, Google, and Facebook have acquired over 200 of the worlds leading AI technologies in recent years. Take for instance Apples acquisition of Lattice Data, a dark data company, for $200 million. Lattice was only founded in 2015 by a Stanford professor, and its amazing technology for understanding unsorted, unformatted data (so called dark data) will no longer be available for any outside company after it vanished inside Apples corporate universe.

So the unfortunate fact for Plant Co. and many medium-sized firms in the world is that the most amazing technologies in AI are off limits now.Element AI goes the other way and helps grow the AI industry by giving mid-sized companies access to new tech resources in startups and researchers.

We are desperate for just any data scientist you can find we already tried everything These are the words of the head of HR at one of Europes largest telecom operators. They arent just saying it is difficult to attract the best AI talent, they arent even getting middle-of-the-road talent.

Most companies are looking at AI as a tool to help them make better decisions. But skilled data scientists are in short supply, and building machine learning in-house means reinventing the wheel instead of solving the parts of the problem that matter most.

Having recruited and worked with AI specialists over the last four years, we can attest that attracting the best AI talent is not easy. They often do not want to work fulltime for one company as they are easily bored and want to be challenged. They are also sought after by Google, Facebook, and the like for placements in New York, Berlin, and California, making it difficult for Plant Co. to get them to show up for work in Karlsruhe (no offense, it is a beautiful town).

So, if a mid-sized firm in rural France wants to hire a world-class AI talent, good luck, you have better chances playing the lottery!

The third challenge for mid-sized firms is that AI still resides in the domain of the techies and scientists. Available solutions are either too technical or too broad. They dont answer business questions such as How can I increase my Just-in-Time manufacturing process? or Can I help lower production costs?. AI isnt available to the business leaders but stuck in the tool basket of the tech department.

Business people often dont know what raw data they even have in their firm, where it is stored, in what format, and what you could do with it.

Giving them an API or open-source code addresses the needs of their CTO but not the business person who has a clear need and a budget for AI. Element AI is working to democratize artificial intelligence and hold the hands of the non-tech executive.

This AI revolution brings back memories of the Internet avalanche in the 1990s when first only the geeks and programmers used HTML. Then suddenly Netscape made it usable for non-tech people and WordPress gave us easy drag-drop tools so we could create our own company pages as millions do every month.We need the equivalent of Netscape and WordPress for AI in order to access and use AI easily and simply.

This brings us to why Element.AI is the most undervalued company in 2017 yes, you heard right, we said undervalued. The companys strategy of creating an ecosystem of technologies and talent combined with handholding AI-strategy consulting for business people is the solution to the three challenges mentioned above. Its what every mid-sized company and low-tech firm needs, whether they are in Ohio or Germany.

We predict there will be more versions of Element.AI emerging that could focus on specific sectors (say manufacturing in Germany and France) or other horizontal niches or geographies.

Elements launch to bring the world of AI to low-tech firms will be our Netscape moment when we look back 10 years from now.

Simon Schneider is CEO of Zyncd and UK Director of ECSI Consulting.

Francis Bland is a founding partner of Predator Capital Partners. He was previously VP at X-Prize Foundation.

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Why Element.AI's $102 million round is just the beginning - VentureBeat

Modern artificial intelligence is smart enough to beat humans at chess, understand speech, and even drive a car.

But one area where machine-learning algorithms still struggle is explaining to humans how and why theyre making particular decisions. That can be fine if computers are just playing games, but for more serious applications people are a lot less willing to trust a machine whose thought processes they cant understand.

If AI is being used to make decisions about who to hire or whether to extend a bank loan, people want to make sure the algorithm hasnt absorbed race or gender biases from the society that trained it. If a computer is going to drive a car, engineers will want to make sure it doesnt have any blind spots that will send it careening off the road in unexpected situations. And if a machine is going to help make medical diagnoses, doctors and patients will want to know what symptoms and readings its relying on.

If you go to a doctor and the doctor says, Hey, you have six months to live, and offers absolutely no explanation as to why the doctor is saying that, that would be a pretty poor doctor, says Sameer Singh, an assistant professor of computer science at the University of California at Irvine.

Singh is a coauthor of a frequently cited paper published last year that proposes a system for making machine-learning decisions more comprehensible to humans. The system, known as LIME, highlights parts of input data that factor heavily in the computers decisions. In one example from the paper, an algorithm trained to distinguish forum posts about Christianity from those about atheism appears accurate at first blush, but LIME reveals that its relying heavily on forum-specific features, like the names of prolific posters.

Developing explainable AI, as such systems are frequently called, is more than an academic exercise. Its of growing interest to commercial users of AI and to the military. Explanations of how algorithms are thinking make it easier for leaders to adopt artificial intelligence systems within their organizationsand easier to challenge them when theyre wrong.

If they disagree with that decision, they will be way more confident in going back to the people who wrote that and say no, this doesnt make sense because of this, says Mark Hammond, cofounder and CEO of AI startup Bonsai.

Last month, the Defense Advanced Research Projects Agency signed formal agreements with 10 research teams in a four-year, multimillion-dollar program designed to develop new explainable AI systems and interfaces for delivering the explanations to humans. Some of the teams will work on systems for operating simulated autonomous devices, like self-driving cars, while others will work on algorithms for analyzing mounds of data, like intelligence reports.

Each year, well have a major evaluation where well bring in groups of users who will sit down with these systems, says David Gunning, program manager in DARPAs Information Innovation Office. Gunning says he imagines that by the end of the program, some of the prototype projects will be ready for further development for military or other use.

Deep learning, loosely inspired by the networks of neurons in the human brain, uses sample data to develop multilayered sets of huge matrices of numbers. Algorithms then harness those matrices to analyze and categorize data, whether theyre looking for familiar faces in a crowd or trying to spot the best move on a chess board. Typically, they process information starting at the lowest level, whether thats the individual pixels from an image or individual letters of text. The matrices are used to decide how to weight each facet of that data through a series of complex mathematical formulas. While the algorithms often prove quite accurate, the large arrays of seemingly arbitrary numbers are effectively beyond human comprehension.

The whole process is not transparent, says Xia Ben Hu, an assistant professor of computer science and engineering at Texas A&M and leader of one of the teams in the DARPA program. His groupaims to produce what it calls shallow models: mathematical constructs that behave, at least in certain cases, similarly to deep-learning algorithms while being simple enough for humans to understand.

Another team, from the Stanford University spin-off research group SRI International, plans to use what are called generative adversarial networks. Those are pairs of AI systems in which one is trained to produce realistic data in a particular category and the other is trained to distinguish the generated data from authentic samples. The purpose, in this case, is to generate explanations similar to those that might be given by humans.

The team plans to test its approach on a data set called MovieQA, which consists of 15,000 multiple choice questions about movies along with data like their scripts and subtitled video clips.

You have the movie, you have scripts, you have subtitles. You have all this rich data that is time-synched in situations, says SRI senior computer scientist Mohamed Amer. The question could be, who was the lead actor in The Matrix?

Ideally, the system would not only deliver the correct answer, it would let users highlight certain sections of the questions and answers to see the sections of the script and film it used to figure out the answer.

You hover over a verb, for example, it will show you a pose of the person, for example, doing the action, Amer says. The idea is to kind of bring it down to an interpretable feature the person can actually visualize, where the person is not a machine-learning developer but is just a regular user of the system.

Steven Melendez is an independent journalist living in New Orleans.

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Why The Military And Corporate America Want To Make AI Explain Itself - Fast Company

Artificial intelligence "is rewiring our modern conception of healthcare delivery," according to a new Accenture report that shows an array of clinical AI applications are already well on their way to saving the industry $150 billion over the next 10 years.

In the shorter term, the report forecasts a 40 percent compound annual growth rate between now and 2021, with acquisitions of AI startups proceeding at a feverish pace.

[Also:With machine learning and AI in healthcare, can you speak the language?]

The technology represents "a significant opportunity for industry players to manage their bottom line in a new payment landscape," according to the report, which examined 10 different AI applications, ranked by their potential for cost savings. Robot-assisted surgery $40 billion Virtual nursing assistants $20 billion Administrative workflow assistance $18 billion Fraud detection $17 billion Dosage error reduction $16 billion Connected machines $14 billion Clinical trial participant identifier $13 billion Preliminary diagnosis $5 billion Automated image diagnosis $3 billion Cybersecurity $2 billion "As these, and other AI applications gain more experience in the field, their ability to learn and act will continually lead to improvements in precision, efficiency and outcomes," said Accenture researchers.

But as the industry rushes headlong to embrace a technology that "thinks and pays for itself," there are some important considerations to keep in mind, according to the report.

[Also:As AI spreads through healthcare, ethical questions arise]

First, it's key to plan for the way the healthcare workforce will be affected as the nature of employment changes with automation. It's a tricky challenge to "make the best use of both humans and AI talent," but it can be done. "For instance, AI voice-enabled symptom checkers triage patients to lower-cost retail or urgent care settings and direct patients to the emergency department only when emergency care is necessary."

Sound organizational strategy is also critical. To make the most AI, hospitals should make expertise in emerging technology a central part of their structure and governance: "For instance, assigning a lead who is tasked with keeping apprised of AI adoption within the organization," according to the report. "Governance and the operating model should also be revamped to align with an AI-enabled organization."

Patient engagement is another emerging avenue for artificial intelligence: Consumers are increasingly used to AI tools, and see them as useful and valuable. AI "can magnify care reach by integrating health data across platforms," said researchers. "However, as new technology is introduced, various data sources must be connected to enable a seamless experience for patients."

Finally, security is a critical concern. "Parties in the ecosystem will need to work together in an ethical way, and be secure in how they manage critical information on patients," according to the report. "As AI delivers benefits of greater efficiency, transparency and interoperability, organizations must maintain a clear focus on informational security."

Twitter:@MikeMiliardHITN Email the writer: mike.miliard@himssmedia.com

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Healthcare AI poised for explosive growth, big cost savings - Healthcare IT News