Artificial intelligence is transforming the world around us | NCET Biz Tips

While HAL and sinister robots set on world domination may not be in our future, widespread applications of artificial intelligence, or AI for short, are …
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“I’m sorry, Dave. I afraid I can’t do that.”

While HAL and sinister robots set on world domination may not be in our future, widespread applications of artificial intelligence, or AI for short, are already here.

To learn more about AI, we asked four of Northern Nevada’s top AI experts to share some datapoints and predictions:

  • Emily Hand, Assistant Professor of Computer Science, UNR
  • Matt Jamison, Technology Consultant, The Gunter Group
  • Meg VanDeventer, Partner Engagement Lead, Blockchains
  • Tom Van Ruiten, Digital Marketing Manager, Noble Studios

Here’s what they said:

Market research firm Tractica estimates that the Global Artificial Intelligence software market size is expected to grow by approximately 154% each year from $9.5 billion in 2018 to a forecasted $118.6 billion by 2025.

Between 2016 and 2025, the top five AI worldwide segments — ranked by revenue — will be:

  1. Machine/vehicular object detection/identification/avoidance
  2. Static image recognition, classification and tagging
  3. Patient data processing
  4. Algorithmic trading strategy performance improvement
  5. Geolocation

According to Statista sources Accenture and Frontier Economics, AI has the potential to increase economic productivity of the United States from 2.6 percent in 2019 to 4.6 percent by 2035. While the U.S. is anticipated to lead the innovation’s impact on a global scale, other regional growth hubs include Finland, the United Kingdom, Sweden and the Netherlands, among others.

More: Domo arigato, Mr. Roboto — Welcome to Reno-Sparks | Kazmierski

Advanced data collection has led us to a period where websites are delivering dynamic, constantly changing experiences that are personalized to individuals. We see this to some degree now on many websites like Amazon, but we are moving to a point where two different individuals might not even experience recognizably similar versions of a landing page, as sites are learning to modify page structure and content instantaneously based on behavioral, demographic and other third-party data.

Machine learning as it relates to digital marketing is all about optimizing results for goals. Algorithms are not subjective or emotional. Is your goal to get visitors to your website, or is it to get them to purchase once they land on your website? Are you going after more clicks on your ads or a higher return on ad spend? The machine logic doesn’t struggle with competing goals like we do; it’s either A or B, not some dynamic combination that fluctuates between weekly management meetings.

More: US has plans for using artificial intelligence. Now what?

To take advantage of current AI capabilities, you must be able to prioritize your website’s marketing goals, especially as it relates to the buyer’s journey from awareness, to research, to purchase. The AI algorithms optimize results over time, with more users/data leading to better results. Be prepared to carefully prioritize your organizational goals and stick with it as the machine does its thing.

Learn more about current and future applications of artificial intelligence and what they could mean for your personal and professional lives with our panel of experts at NCET’s luncheon on July 24. More info and tickets at NCETspecial.org.

Dave Archer is resident and CEO of NCET, a member-supported nonprofit that produces educational and networking events to help people explore business and technology.

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New UAE-based institute to boost students’ Artificial Intelligence skills

A new institute dedicated to teaching Artificial Intelligence (AI) applications to university students has been launched in Abu Dhabi on Monday (July …

A new institute dedicated to teaching Artificial Intelligence (AI) applications to university students has been launched in Abu Dhabi on Monday (July 15). This is the first-of-its-kind-institute in the UAE will also train government and industries in AI science and applications.

With a Dh160 million five-year-fund for AI projects, Khalifa University of Science and Technology launched the Artificial Intelligence and Intelligent Systems Institute (AI Institute) which will focus on AI, data science, robotics, next generation networks, semiconductor technologies and cybersecurity.

Six research centres under one umbrella

The AI Institute will bring all the university’s research in robotics, artificial intelligence (AI), cyber-security, data science and information and communication technologies under a single umbrella.

“Khalifa University’s AI Institute, a single umbrella that gathers activities of six research centres, reflects our commitment to research in next generation digital technologies that are priority areas for the UAE’s economy,” Dr Arif Sultan Al Hammadi, executive vice-president of Khalifa University of Science and Technology said during the launch of the AI Institute.

“This is the first institute in the UAE focused on AI applications and will train government and industries in AI science and applications. It will also conduct research that will benefit government and industry sectors,” he added.

The university also unveiled two initiatives – a programme in AI and the Mohammed Bin Zayed International Robotics Challenge (MBZIRC) that will be held in February 2020 – on Monday to further advance innovation in robotics and artificial intelligence.

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Defective potassium channels cause headache, not body pain

New research, computational neuroscience, theories and methods are also published. About The Society for Neuroscience. The Society for …

IMAGE

IMAGE: Top: Normal distribution of TRESK channels (green) in facial pain sensory neurons. Bottom: Absence of channels in the knock-out mouse model. view more

Credit: Guo et al., eNeuro 2019

Defective potassium channels involved in pain detection can increase the chance of developing a headache and could be implicated in migraines, according to research in mice published in eNeuro.

A type of potassium channel called TRESK is thought to control the excitability of peripheral sensory neurons that detect pain, heat, cold, and touch. Even though these channels are found throughout the neurons sensing both body and facial pain, channel mutations are linked only with headaches and not body pain.

Yu-Qing Cao and colleagues at Washington University in St. Louis analyzed a knock-out mouse with defective TRESK channels and measured the resulting neural activity. The researchers found that only facial pain receptors were more excitable, and that the sensory neurons had more spontaneous activity. Using behavioral tests, the scientists observed that the knock-out mice showed increased sensitivity to temperature and touch stimuli on their faces, as well as more headache-related behaviors, but no body pain behaviors.

These results indicate that TRESK channels have cell-specific roles and are responsible for regulating pain in facial sensory neurons, making them a target for migraine treatment research.

###

Manuscript title*: TRESK K+ Channel Activity Regulates Trigeminal Nociception and Headache

*A preprint of the manuscript is available on bioRxiv

Please contact media@sfn.org for full-text PDF and to join SfN’s journals media list.

About eNeuro

eNeuro, the Society for Neuroscience’s open-access journal launched in 2014, publishes rigorous neuroscience research with double-blind peer review that masks the identity of both the authors and reviewers, minimizing the potential for implicit biases. eNeuro is distinguished by a broader scope and balanced perspective achieved by publishing negative results, failure to replicate or replication studies. New research, computational neuroscience, theories and methods are also published.

About The Society for Neuroscience

The Society for Neuroscience is the world’s largest organization of scientists and physicians devoted to understanding the brain and nervous system. The nonprofit organization, founded in 1969, now has nearly 37,000 members in more than 90 countries and over 130 chapters worldwide.

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AI enables ‘hybrid drones’ with the attributes of both planes and helicopters

Researchers have developed an AI system enabling ‘hybrid drones’ which combine the attributes of both planes and helicopters.

Researchers have developed an AI system enabling ‘hybrid drones’ which combine the attributes of both planes and helicopters.

The propeller-forward designs of most drones are inefficient and reduce flight time. Researchers from MIT, Dartmouth, and the University of Washington have proposed a new hybrid design which aims to combine the perks of both helicopters and fixed-wing planes.

In order to support the new design, a new AI system was developed to switch between hovering and gliding with a single flight controller.

Speaking to VentureBeat, MIT CSAIL graduate student and project lead Jie Xu said:

“Our method allows non-experts to design a model, wait a few hours to compute its controller, and walk away with a customised, ready-to-fly drone.

The hope is that a platform like this could make more these more versatile ‘hybrid drones’ much more accessible to everyone.”

Existing fixed-wing drones require engineers to build different systems for hovering (like a helicopter) and flying horizontally (like a plane). Controllers are also needed to switch between.

Today’s control systems are designed around simulations, causing a discrepancy when used in actual hardware in real-world scenarios.

Using reinforcement learning, the researchers trained a model which can detect potential differences between the simulation and reality. The controller is then able to use this model to transition from hovering to flying, and back again, just by updating the drone’s target velocity.

OnShape, a popular CAD platform, is used to allow users to select potential drone parts from a data set. The proposed design’s performance can then be tested in a simulator.

“We expect that this proposed solution will find application in many other domains,” wrote the researchers in the paper. It’s easy to imagine the research one day being scaled up to people-carrying ‘air taxis’ and more.

The researchers will present their paper later this month at the Siggraph conference in Los Angeles.

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, Blockchain Expo, and Cyber Security & Cloud Expo.

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How Artificial Intelligence Might Impact Radiology

Siemens has long focused on automation as a way to make diagnostic equipment faster and more efficient. Artificial intelligence (AI) is the latest tool …

Siemens has long focused on automation as a way to make diagnostic equipment faster and more efficient. Artificial intelligence (AI) is the latest tool the company is leveraging to do that, according to Wesley Gilson, artificial intelligence lead for Siemens Healthineers in North America.

The company’s early focus on operational aspects of AI, however, is part of a longer-term approach that Gilson and the vice president of Siemens’ digital services business in North America, Peter Shen, plan to describe on July 21 at The Association for Medical Imaging Management (AHRA) annual meeting in Denver. The objective of their talks, scheduled for a Siemens-sponsored exhibitor symposium will be “to educate our audience on artificial intelligence,” Gilson told Imaging Technology News. To tell the audience “where we see AI making an impact in health care particularly in radiology today and in the future.”

Siemens looks at AI, he said, as a kind of continuum — one that can be addressed through “a tiered approach.” Fundamentally the company is leveraging AI to make its radiology equipment generate reproducible results faster. Such operational improvements could bear early fruit, boosting productivity by speeding up clinical workflow; preventing diagnostic errors; even reducing missed billing opportunities, according to Gilson. In this way, AI might help the company achieve long-held objectives, including increased efficiency and accelerated diagnoses.

“Having those touch points in the early stages with regards to automation, operational reproducibility — those are small targets that you can hit,” he said.

Aiming at Small Targets

Siemens is focusing early on these targets not because they are easy, Gilson said, but because doing so might produce benefits for clinicians and patients in both the short- and long-term. Siemens is looking into questions about “how we can acquire data faster and acquire (them) more reproducibly; how we can then take that data and be able to draw conclusions (from them),” he said. The answers could ultimately make radiology more precise.

Greater precision may also come from increased consistency from one scan to the next — a natural byproduct of the standardization of imaging. Through the improvement of day-to-day operations, AI could have a huge impact, Gilson said. Over the long haul, this impact could broaden, if AI makes radiology even more data driven. “Radiology is already data-driven, but AI may take it to the next level with regards to its ability to draw even more and more information out of the data that is being collected,” he said

Eventually AI might make prognostic risk scores increasingly meaningful, he said, explaining that smart algorithms might see patterns in data about disease progression that people alone could not recognize. “There is great optimism that AI will be able to help us understand disease processes,” he said.

How AI Might Extract and Use Data

The underlying concept is that AI might be used to extract information from the ever-growing amount of healthcare information being collected both for individuals and patient populations. This information then might be used by physicians to assess patient prognoses in terms of risks and by patients to influence their futures for the better.

Eventually, AI could help integrate diagnostic radiology into the clinical decision-making mainstream. But Gilson advises caution.

“Once you start moving into the space of interpreting the data, that is where it starts to be a bit of a controversy,” he said.

Greg Freiherr is a contributing editor to Imaging Technology News (ITN). Over the past three decades, he has served as business and technology editor for publications in medical imaging, as well as consulted for vendors, professional organizations, academia, and financial institutions.

Editor’s note: This article is the second piece in a content series by Greg Freiherr covering The Association for Medical Imaging Management (AHRA) annual meeting in Denver. The first article, How Standardizing Protocols Can Save Time and Money, can be found here.

Related content:

How Standardizing Protocols Can Save Time and Money

Technology Report: Artificial Intelligence

PODCAST: How to Make Artificial Intelligence a Success in Medicine

PODCAST: Radiologists Must Understand AI To Know If It Is Wrong

PODCAST: Is Artificial Intelligence The Doom of Radiology?

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