Monthly Archives: September 2019

Drug discovery needed to change. The low-hanging fruit had been harvested, but the biopharma industry, in the words of Deep Genomics CEO Brendan Frey, is still shoving the tree until an apple falls.

Making drugs has traditionally been a gambling game. Big Pharma is throwing a stick into the tree and seeing what happens, Frey told FierceBiotech. Its like the Big Pharma companies come into a casino, put a million-dollar coin into a slot machine and with some probability like 10% or something, they get a win.

Instead of gambling to get at the fruit higher up on the tree, Frey built Deep Genomics, a company using artificial intelligence to discover new disease targets as well as the best compounds to drug them. He calls it building a ladder.

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The Toronto-based biotech has been quietly plugging away since 2015 on its AI Workbench, a technology driven byyou guessed itAI to discover genetic medicines. It uses more than 20 machine learning systems that have been carefully validated and tested and trained on public and proprietary data to screen disease-causing mutations in search of new drug targets.

RELATED: Unlocking small data, the 'next frontier' in drug discovery

We have built a system that within two hours can scan over 200,000 pathogenic patient mutations and automatically identify potential drug targets, Frey said. Instead of chasing known targets or getting into a hot therapeutic area, Deep Genomics is letting the system do the choosingand it landed on Wilson disease, a rare genetic disorder that has no disease-modifying treatments.

People with Wilson disease cant clear excess copper, which ends up accumulating in various tissues such as the liver and the brain. Current drugs focus on lowering copper levels by stopping the body from absorbing the copper in food or making it get rid of copper through urine. If untreated, Wilson disease is fatal.

There is no treatment that helps patients regain the ability to remove copper, because researchers struggled to understand how the underlying mutation led to Wilson disease.

Mutations can cause disease in different ways and humans are very familiar with some of those ways, such as when a mutation changes a protein so it no longer works correctly, Frey said. But there is a huge space of mutations out there that dont cause a problem through that type of mechanism.

RELATED: GSK hires Branson from Genentech to boost AI team

And that is where AI comes in. Deep Genomics system figured out that the mutation changes an amino acid in ATP7B, a copper-binding protein that is absent in Wilson patients. But it also predicted that this change should not affect how the protein works. The AI eventually discovered that the mutation disrupts an instruction in the genome that tells cells how to make that protein, causing it to not be made at all.

The platform identified a dozen potential drug candidates, and Deep Genomics took them to the lab to make sure they worked the way the AI predicted they would. After some tolerability and pharmacokinetics experiments, the company declared DG12P1 its first pipeline candidate that it would advance toward IND.

We view this first declaration as ushering in a new era in drug discovery, for us, Deep Genomics, as well as more broadly, Frey said. Because this is really the first time that AI has really helped many different steps of drug discovery.

Other companies have deployed this technology at various stages of discovery. Insilico Medicine recently published a study showing that in just three weeks, its algorithm turned up 30,000 new compounds to target discoidin domain receptor 1, or DDR1, which is involved in fibrotic diseases.Atomwises technology screens virtual compounds to identify new drugs that might take years to find using traditional discovery methods. And Insitro is using in vitro systems to predict what drug developers would see in a human clinical model. It is these models that would propose drug targets and predict how patients, or specific subgroups of patients, will respond to certain treatments.

For Deep Genomics, it took 18 months to get from a brand-new target to a drug candidate. And it may nominate new programs even faster.

RELATED: Stealthy Insitro opens upstarting with Gilead deal worth up to $1.05B

We expect to declare two candidates this year, we expect to declare at least twice as many next year and at least twice as many as that the year after that, he said.

The challenge will be how Deep Genomics manages to develop all of these candidates and get them to patients.

We going to do that through a combination of developing them internally and also partnering them out. Its something we would do very carefully because we want to make sure that drugs are developed efficiently, Frey said.

Will Deep Genomics approach become the ladder biopharma needs to reach further up the apple tree? Time will tell.

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Deep Genomics reveals its program: The first AI-discovered drug candidate - FierceBiotech

From health care to the transportation industry, FOX Business Maria Bartiromo looks at how artificial intelligence (AI) isshaping the future of society.

In February the U.S. government launched an American AI initiative, which aims to stimulate AI development. The governments investments in unclassified R&D for AI technologies is up 40 percentsince 2015 and for the first time in history, President Trumps fiscal year2019 budget requests to designate AI and unmanned autonomous systems a priority.

However, some say the problem is that China spends much more on AI investment and financing. In 2017, AI spending hit $39.5 billion, with China accounting for 70 percent of the expenditures.

AI is growing by leaps and bounds. Were using it to help control drones in the air, as well as in the sea, said retired four-star general Jack Keane. In fact, I think their advancement is greater than ours.

Palmer Luckey, the founder Anduril Industries, a defense technology company that builds autonomous drones and sensors for military applications, believes that ethics put the U.S. at a strategic disadvantage.

We're not willing to play dirty, we're not willing to let machines decide what targets to kill and not kill entirely on their own with no human supervision, he said.Russia and China don't have a problem with any of those things.

AI is also changing society in other ways, liketransforming the health care industry. The AI health care market is expected to grow from $2.1 billion to $36.1 billion in 2025.

IBM CEO Ginni Rometti said it can be used to potentially save peoples lives. The company uses AI computer Watson to build knowledge on diseases from Parkinsons to cancer. IBM has been a big investor in health care, which is an industry in dire need of AI, she said.

We've been working away on Watson for health, and in particular, we worked on oncology we're now at 300 hospitals in over a 125,000 patients around the world where AI has helped the doctor identify the diagnosis and the appropriate treatment, she said. We are now at Mayo Clinic, as an example, trained for clinical trial matching for breast cancer. Almost every patient goes through to see if there's a clinical trial, which is an example. So these are things that you didn't realize before how either infrequently they were done or not done with precision.

However, with the rise of AI also comes the fall of other types of jobs. According to areportby Oxford Economics, by 2030robotswill displace 20 million jobs.

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FormerGoogleChinapresidentKai-Fu Lee, said within the next 15 years, 40 percentof jobs, especially those that are routine and rely on data, including mortgage brokers, radiologist, and eventually truckers, will be replaced.

Routine white-collar jobs like customer service, telemarketing, loan officers and tellers and jobs like that will be the first to be challenged, he said. Then repetitive blue-collar jobs like dishwasher, assembly line workers, and a little bit later, drivers, also will be challenged.

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How artificial intelligence is shaping the future of society - Fox Business

Highlights

AMSTERDAM, Sept. 27, 2019 /PRNewswire/ -- Inspur, a leading data center and AI full-stack solutions provider, today announced two AI-technology driven open computing systems. TheX-MAN 4.0, developed with Baidu, is the world's first OAI (Open Accelerator Infrastructure) compliant and liquid cooling rack-scale AI computing product optimized specifically for deep neural network applications. The Inspur OAI UBB system, meanwhile, is a 21-inch Full-Rack OAM solution delivering efficiency, flexibility and management.

Workloads in data centers are growing more diverse and complex with artificial intelligence and other emerging technologies and applications spreading rapidly. Plus, Internet companies are struggling with AI's increasing hardware complexityintegrating an AI accelerator typically takes 6 to 12 months. That is to say, improving the flexibility of AI hardware is imperative. The OAI specificationled by Baidu, Facebook and Microsoft in the OCP (Open Compute Project) communityunifies the technical specifications of the accelerator module and simplifies the design complexity of the AI accelerator system, thereby shortening time to market for the hardware system. With the joint launch of X-MAN 4.0 with Baidu and as the key promoter to realize OAI UBB development with OCP, Inspur has rapidly followed up on this standard with solutions, in order to help customers accelerate business innovation.

X-MAN 4.0provides powerful performance with strong scalability and interconnect technology

X-MAN 4.0, as the fourth generation of Baidu's X-MAN series of full-rack AI computing products, is the latest example of Inspur's leadership in providing the world's most powerful AI server platforms. X-MAN 4.0 provides a variety of performance, flexibility, cost and other advantages over traditional graphics processing unit (GPU) servers.

X-MAN 4.0's strong scalability and interconnect technology provide powerful performance for data center users. Not only does the system include eight AI accelerators in a single box and scale to 32 AI accelerators per rack, but also the boxes are interconnected through QSFP-DD cables to minimize communication latency across nodes. X-MAN 4.0 is the first in Baidu's series of full-rack AI computing products to introduce OAM-compliant accelerators; besides, accelerator resources can be specified in a software-defined manner. Together, these features mean flexible, multi-vendor support for supporting AI applications of different workloads. UBB boards with various topologies can be configured directly and flexibly according to the different application requirements.

Baidu's X-MAN series products had several pioneering design concepts such as hardware disagregation in X-MAN 1.0, liquid cooling in X-MAN 2.0 and modular and open design in X-MAN 3.0. Baidu contributed such design concepts to the OCP community, led the OAI project and also designed the first OAI compliant and liquid cooling AI computing system. As one of Baidu's key partners, Inspur has been actively participating in the development of X-MAN series product.

OAIUBBsystem delivers breakthrough efficiency, flexibility and management

The newly launched OAIUBB system also delivers breakthrough efficiency, flexibility and management. The 21-inch Full-Rack OAIsolution provides simplified inter-module communication to scale up and input/output bandwidth to scale out to support disparate network architectures through OAM direct connection. And, Inspur can feature two out of the three OCP interconnect topologies: Hybrid Cube Mesh (HCM) and Fully Connected (FC).

The new UBB system and X-MAN 4.0 highlight Inspur's longstanding commitment to leadership in open computing technologies. Moreover, with these launches, Inspur is enabling its global internet customers to leverage OAIto bring their innovative new AI solutions to market quickly.

"Inspur continues to pioneer the technologies that enable next-generation AI applications," said Peter Peng, Inspur Group senior vice president. "As a member of the OCP, Open 19 and ODCC global open computing standards organizations, Inspur has always been an active promoter of open source technology to help users build open data centers. We work to facilitate cooperation among all key open standards to help our customers accelerate business innovation and more efficiently and effectively bring about next-generation AI applications."

About Inspur

Inspur is a leading provider of data center infrastructure, cloud computing, and AI solutions, ranking among the world's top 3 server manufacturers. Through engineering and innovation, Inspur delivers cutting-edge computing hardware design and extensive product offerings to address important technology arenas like open computing, cloud data center, AI and deep learning. Performance-optimized and purpose-built, our world-class solutions empower customers to tackle specific workloads and real-world challenges. To learn more, please go to http://www.inspursystems.com.

SOURCE Inspur Electronic Information Industry Co., Ltd

http://www.inspursystems.com

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Inspur and Baidu Jointly Launched World's First OAI Compliant Open AI Computing Solution - PRNewswire

Google has released a huge database of deepfake videos that it's created using paid actors. It hopes the database will bolster systems designed to detect AI-generated fake videos.

With the 2020 US Presidential elections looming, the race is on to build better systems to detect deepfake videos that could be used to manipulate and divide public opinion.

Earlier this month, Facebook and Microsoft announced a $10m project to create deepfake videos to help build systems for detecting them.

SEE: How to implement AI and machine learning (ZDNet special report) | Download the report as a PDF (TechRepublic)

As Facebook noted at the time, one of the issues with detection systems is that there isn't much material to work with to create one and to act as a benchmark for testing. That project is also using paid actors to create a larger dataset.

Google and Alphabet's Jigsaw are contributing a dataset of 3,000 manipulated videos with 28 actors to the new FaceForensics benchmark, which aims to become an automated benchmark for facial-manipulation detection.

This benchmark is being developed by researchers at Technical University of Munich and the University Federico II of Naples. Google calls its videos the Deep Fake Detection Dataset. Details about the FaceForensics benchmark can be accessed on the researchers' GitHub page.

"To make this dataset, over the past year we worked with paid and consenting actors to record hundreds of videos. Using publicly available deepfake-generation methods, we then created thousands of deepfakes from these videos,"said Nick Dufour from Google Research and Jigsaw's Andrew Gully.

"The resulting videos, real and fake, comprise our contribution, which we created to directly support deepfake detection efforts."

Google earlier this year also release a dataset to help deepfake audio detection and to prevent cases such as the executive who was recently duped into transferring over $200,000 to a fraudster who'd synthesized his boss's voice.

Google and Alphabet's Jigsaw are contributing a dataset of 3,000 manipulated videos with 28 actors.

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Google's war on deepfakes: As election looms, it shares ton of AI-faked videos - ZDNet

Bengaluru: At least 10 new AI-based jobs are being created for every 100 positions made redundant in traditional technology, experts in the field have said.

However, the new roles are not being created as quickly as the positions that are eliminated and companies are increasingly training talent to fill the gap in artificial intelligence skills.

This was the key takeaway from ETs recent conversation on Technology Talent Equilibrium with Sridhar Mitta, founder of digital technology services provider NextWealth Entrepreneurs; Prakash Mallya, the head of Intel India, and Supriyo Das, vice-president, Wipro Technologies.

If 100 jobs are cut, 10 jobs are created. The people who are losing jobs have different skills, and theyre going away. People are getting new jobs on different skillsets, Mitta said.

Intel has started initiatives to teach AI in schools, said Mallya.

I guess, training skills outside the industry requires a collective ecosystem and efforts across different companies, he said.

You need to approach it in different ways. You need to reach out to industry, developers, startups. How do we include formal education on AI to build mindset, have some level of skills so that they understand the new world? We have the AI for Youth initiative, which is 254 hours, four-stage (that) we are rolling out for 10 schools, Mallya explained.

To develop AI-ready talent, Intel India has trained more than 1,50,000 developers, students and professors since 2017.

The global opportunity of AI was estimated at about $15.7 trillion by 2030, according to a PwC report last year. Technology services companies are increasingly using AI at the core, raising the demand for talent.

The prediction is definitely there will be a shortfall of skills. We need to categorise where you need most right, and what kind of jobs will get created, said Das of Wipro.

From our organisations perspective, we do not see AI as a particular set of skills for a particular set of people. AI is needed everywhere, he added.

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Only 10 jobs created for every 100 jobs taken away by AI - Economic Times

a new PSA in america uses artificial intelligence to create a composite portrait of hunger based on photos of 1,000 americans who cant afford to stay fed. the visage aims to challenge peoples perception of hunger as a problem faced only by those living on the streets.

US-based relief group feeding america worked with creative agency leo burnett to create the campaign, which is called I am hunger in america. it features a realistic-looking spokewoman, engineered using data from the united states department of agriculture (USDA).

images courtesy leo burnett

it includes 28,000 photos of some of the 1 in 8 americans who are struggling with hunger, which the creative team used to generate a composite before overlaying onto a real person. the campaign also includes a short film of the spokeswoman who tells real stories of food insecurity as her features morph to reflect the various demographics suffering from hunger.

forty million americans suffer from hunger. despite its prevalence, theres a deep misunderstanding around the issue of hunger that makes it difficult to recognize, a description of the project on the leo burnett website explains. we tend to think of hunger in stereotypes, images of the homeless or third world countries. in reality, hunger is much closer to home, affecting 1 in 8 people in the USpeople we potentially encounter every day.

the agency used a generative adversarial network, or GAN, to create the lifelike data-driven portrait. a gan is a cutting-edge form of machine learning in which one neural network hones artificial images until another can no longer tell the difference between them and real-life photos.

this visual data was cross-referenced with a 2018 usda study to identify more specific demographic components including ethnicity, age and sex and narrowed down to 1,000 photos. this representative image set data was then used by AI to generate a unique face that shows us what hunger looks like in America.

in using AI in this way, the campaign not only creates a statistically accurate and unbiased representation, but also one that is empathetically and unexpectedly familiar, reminding americans that hunger can happen anywhere and that recognizing it is the key to making it disappear, the agency adds.

the portrait of hunger campaign includes television and radio spots as well as print, outdoor and digital ads. to learn more about the campaign and the hunger epidemic, visit I am hunger in america.

project info

name: I am hunger in america

creative agency: leo burnett chicago

kieron marchese I designboom

sep 26, 2019

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AI created a 'portrait of hunger' by scanning the faces of 1,000 people in need - Designboom

A package of new guidance documents from the FDA describes what and how the agency plans to regulate as software designed to aid clinical decision-makingsuch as programs that provide documentation and diagnostic supportor that call up medically relevant reference information and recommendations based on patients particular case.

The guidances also include updated, finalized policies for the regulation of smartphone-based medical apps, as well as manufacturers use of so-called off-the-shelf commercial software in medical devices.

The FDA defines software for clinical decision support, or CDS, as tech that can provide doctors, patients or caregivers with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care.

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In its latest draft guidance, the agency said it plans to apply a risk-based strategy for enforcing device-related requirements. It does not intend to regulate certain types of low-risk softwaresuch as programs designed to help inform patients and caregivers in managing non-serious conditions without the help of a doctorespecially when those users can independently check and understand the basis for the programs recommendations.

Instead, the FDA said it plans to focus oversight on higher-risk software functions, including those used in serious or critical situationsas well as machine learning-based algorithms, where the programs logic and inputs may not be fully explained to the user.

RELATED: FDA lays out plans for a new review framework for AI and machine learning-based devices

For example, this would include an artificial intelligence system that identifies hospitalized patients with type 1 diabetes who may be at risk for cardiovascular events. Another would be a learning algorithm that categorizes likely cases of seasonal influenza, using electronic medical records and geographic data, by screening them out from patients with common flu or cold symptoms, the FDA said.

Patients, their families and their health care professionals are increasingly embracing digital health technologies to inform everyday decisions, from tools that more easily report blood glucose levels to smartwatches that can detect atrial fibrillation, Principal Deputy Commissioner Amy Abernethy said in an agency statement.

We believe that an appropriate regulatory framework that takes into account the realities of how technology advances plays a crucial role in the efficient development of digital health technologies, Abernethy said.

In general, one of the agencys oversight goals is to not hinder the development of low-risk but helpful software, where new versions can be developed much faster than the traditional medical devices that have fallen under the agencys purview. Additionally, the 21st Century Cures Act amended the types of software that would otherwise be considered a regulated device.

Were making clear that certain digital health technologiessuch as mobile apps that are intended only for maintaining or encouraging a healthy lifestylegenerally fall outside the scope of the FDAs regulation, she said.

The agency updated four previous final guidances for tech-enabled productscovering mobile medical apps; general wellness and low-risk devices; off-the-shelf software use; and guidance on data systems and medical image storage and communication devicesbringing them all in line with the new definitions set down by the Cures Act, passed in December 2016.

These documents are critical elements of FDAs comprehensive approach to digital health, Abernethy said. We are committed to promoting beneficial innovation in this space while providing appropriate oversight where its merited.

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FDA delivers regulatory guidance on AI software and clinical decision-making aids - FierceBiotech

Using artificial intelligence on the battlefield has drawn debate in recent years amid the Pentagons high-profile effort to train algorithms to pick out people, buildings, and vehicles, amid oceans of drone-gatheredvideo.

On Tuesday, Lt. Gen. Jack Shanahan gave an update on the Pentagons Joint Artificial Intelligence Center at The AtlanticFestival.

Money first: Shanahan said his JAIC (pronounced jake) will see its $89 million budget more than double, to about $200 million, when the next fiscal year begins on Oct. 1. Then, he said, We will seek additional funding through the course of the next five-year budget cycle to begin to show how important it is to bring this across the entire Department ofDefense.

How much money is a little difficult topredict.

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This a little bit of a hard sell sometimes because artificial intelligence in a software-driven age, its hard for me to project out three years from now exactly what projects were going to be working on, Shanahan said. What Id like to do is be held accountable after the fact for did you seek spend your money in the wisest way possible, did you show that you made the right investments and when you didnt, did you fail early and stop a project and moveon.

Asked whether the Pentagons AI goals depend on big tech companies like Google, who withdrew from Maven after thousands of its employees who objected to the work Shanahan replied, It would be extremely hard to do it withoutthem.

How Companies View AIs Role in Defense. Booz Allen Hamilton CEO Horacio Rozanski believes AI will get better when its deployed onto thebattlefield.

Theres a lot of things that are happening now in the lab that are not going to get better until we field them, Rozanski said on Tuesday. Training an AI has to happen in the place where the AI is going to operate. A lab is not where the AI is ultimately going to operate, so its never going to learn, its never going to get trained to the right level until it gets put into an operationalcontext.

But there are key challenges in getting AI to the battlefield, Rozanski said. Among them, the infrastructure for analysts and communications, or lack thereof, on thebattlefield.

Tom Arseneault, BAE Systems president and COO, said modeling might be able to help intesting.

Increasingly, the field is a relative thing, Arseneault said. As we get increasingly digital, were able to model the field in an environment where we can test a little bit more safely and a little less expensively. Systems will be formed out of digital models. We model the environment in which they operate and all of that can happen in a virtualworld.

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Now service officials hope they can get the MQ-25 operating faster thanplanned.

The U.S. will increase air and missile defenses from Iran at the request of Saudi Arabia and the UAE, Pentagon leaderssaid.

Low Earth orbit will soon be awash in small satellites, and the national security community is increasingly concerned about theirsecurity.

Last week, we told you that F-35-maker Lockheed Martin is pitching the Pentagon a five-year maintenance deal to avoid negotiating yearly contracts a move the company says would save billions of dollars. Pratt & Whitney, planes engine maker, sees a value in this type of arrangement, known as a performance-based logistics, or PBL, contract, because it allows the company to lock in long-term supplierdeals.

We think theres incredible value in a PBL. We do agree that going to a longer PBL will allow us as a contractor to provide more value, Matthew Bromberg, president of Pratt & Whitneys Military Engines business, told a few reporters on the sidelines of the Air Force Associations Air, Space and Cyber conference lastweek.

A short PBL is like trying to manage a fleet with your hands tied behind your back, he said. You cant order the parts, you cant invest in the repairs, you cant invest in the sustainment, the tooling, [and] thetraining.

Bromberg said a long-term sustainment contract five year or 10 years I think is the way to go. A 10-year deal is something that we floated, but we dont have an offer out there. That said, the company is talking to the [F-35 joint program office] aboutit.

The commercial sector typically signs seven to 10-year logistics contracts, hesaid.

There he toured the USS Gerald Ford, the Navys newest aircraft carrier, which has been riddled with issues involving its propulsion systems and weapons elevators. Huntington Ingalls Industries said: Esper toured Ford to see the progress being made during the ships post-shakedown availability and to learn more about its weapons-handling innovations and increased warfighting capabilities. Heres a breakdown by Bloomberg about the problems with the ship.

U.S. Air Force combat search-and-rescue crews have been waiting a long time for new helicopters, but they moved a step closer this week. The Air Force cleared the helicopter for production, according to Lockheed Martins Sikorsky, maker of the HH-60W. Four test helicopters are already flying (here are some pictures of them). Now all the helo needs is a name. Rescue Hawk,anyone?

Maybe, say reports about a briefing given by U.S. ambassador David Satterfield to Turkish officials. Satterfield offered on Friday to sell Turkey a Patriot missile defense system and lower tariffs on steel and aluminum, Haberturk and NTV reported, without citing anyone, Bloomberg reports. Local media also reported that the U.S. may unveil a new economic package to boost bilateral trade to $100 billion from about $19billion.

Sen. Lindsey Graham, R-S.C., who has been involved with efforts to pull Ankara back into the F-35 fold, met with Turkeys President Tayyip Erdogan earlier in the week. Were trying to get them back in the F-35 program, he said onSunday.

A meeting between Erdogan and President Trump to discuss the Patriot deal had been expected this week on the sidelines of the U.N. General Assembly meetings in New York but while the two leaders attended a reception on Wednesday, it does not appear that meeting tookplace.

The Trump administration is considering sanctioning Turkey over its buying the S-400 air and missile interceptors from Russia. The U.S. kicked Turkey out of the F-35 program after Ankara began receiving the interceptors this summer. The ban prevents Turkey from buying the planes or making parts forthem.

Announced this week: the State Department cleared a $400 million deal for eight Boeing-made AH-6i attack helicopters, along with Hellfire missiles and a bunch of other weapons for Thailand. State also approved an $86 million deal for Large Aircraft Infrared Countermeasures for Qatar for its two 747-8 head-of-state aircraft AKA, its version of Air ForceOne.

ICYMI: Earlier this month State cleared thesedeals:

Tyler Evans has been named senior vice president of Aerojet Rocketdynes Defense Business Unit. He was previously vice president of the companys Rocket Shop Defense Advanced Programsunit.

General Dynamics appointed General Dynamics NASSCO president Kevin Graney as president of General Dynamics Electric Boat, effective Oct. 1. David Carver, NASSCOs vice president and general manager of repair, will succeed Graney as president of General Dynamics NASSCO. Current Electric Boat president Jeffrey Geiger will retire on Sept.30.

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Will Turkey return to F-35?; DOD AI chief: send money; SecDef tours troubled carrier; and more... - Defense One

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Nurses have always played a critical role at the bedside while bearing witness to numerous changes in technology. In the past 50 years alone, the advancements seem unfathomable to nurses of the not-so-distant past such as test-tube babies, medical lasers, the artificial heart, genome mapping, CT and MRI imaging, angioplasty, dialysis, endoscopic procedures, bionic prosthetics, the internet and health information technology (IT), the electronic health record (EHR), and robotic surgeries. However, as health care races toward telemedicine and artificial intelligence, nurses must strategically position themselves to stay relevant.

In a recent article in Nursing Management, the author stated: Artificial Intelligence (AI) is a branch of computer science dealing with the simulation of intelligent behavior in computers. Combining the experience, knowledge, and human touch of clinicians with the power of AI will improve the quality of patient care and lower its cost. While most nurses have been immersed in the EHR for a decade (or more, depending on the organization), the industry still struggles with quality vs. quantity of input data that will allow for valid information mining, utilizing AI to identify areas of risk, or even the pending decline of a patient.

However, the question of quality data is often reviewed by those who are responsible for an organizations health care IT. Many nurses who jumped into the AI and innovative world of informatics struggle with orchestrating what constitutes valuable data the bedside nurse is required to input versus each disciplines desire to have the information captured. For example, while it is important to the dietician to know which brand of tube feeding was administered to the patient, does it truly add value enough to warrant one more line on the 1,000+ EHR rows for the nurse to capture? Preserving documentation requirements to the essential data points will not only help bedside nurses save precious time, but also allow for predictability models to work in the background to anticipate patients who may decline.

Bedside nurses are the key to AI as it relates to predictability models and telemedicine. Data points such as temperature, blood pressure, and physical assessment values, entered into the EHR in a timely manner, can literally make the difference between life and death as the health information technology is scanning thousands of factors to provide outcome information. Getting nurses onboard with real-time, accurate documentation (not just copying the assessment from the previous shift) is essential. Nurses are viewing AI as telling them what to do instead of using the insights AI provides as part of their clinical decision, states Dan Weberg, PhD, RN. In order for nurses to stay relevant, we need to figure a way to incorporate new technology directly into practice. AI isnt making decisions for us as nurses, its making us super nurses!

Dr. Weberg, who has worked as an innovation specialist for multiple health systems and academic institutions across the U.S., states that nurses need to demand a seat at the innovation table. Each year, more and more institutions are developing smart health apps that directly affect nursing. Yet the absence of the nurse in the concept and design is palpable. Many times, nurses are not brought into a project until it is time to implement the technology, which is a challenging time to make any nurse-recommended changes. When technology is designed and implemented without nursing input, workarounds are created, which can lead to the innovation not being used to its full capacity.

The lack of enthusiasm and embracement of technology maystart in nursing schools, according to Dr. Weberg. There is a gap between thetraditional, old-school methods of teaching students how to be nurses andactual innovation in practice. Academia needs to foster a healthy relationshipbetween nursing and advancing technology if we want to remain relevant. Onewould assume that as nurses are sworn to advocate for and protect patients,embracing technology that improves patient outcomes should be obvious.

When artificial intelligence and modern technology are infused with the art of medicine, patients are safer. In a study conducted by the Institute of Medicine in 2000, it was found that 10% of medical diagnoses were wrong. The report, called To Err is Human, called for technological advances such as physician order entry to reduce the guessing game in deciphering clinician handwriting. Since that report nearly 20 years ago, many studies have proven that there are less medication errors, less adverse drug reactions, and improved compliance to evidence-based practice guidelines with the integration of health information technology.

As health care technology continues to evolve at a rapid pace, it is not just the encompassing profession of nursing that needs to be in on the change. Bedside nurses can strive to become super users in their departments as subject-matter experts in any technology or innovation that is being designed and implemented. Staying one step ahead and developing a mindset of improvement and innovation can be powerful. For the nurses who complain that things are always changing too much who long for the trifold, 2-sided, chart-in-the-color-of-your-shift days, that ship has sailed and good riddance. The future of nursing is aglow with the megahertz light of innovation to improve the lives of nurses, patients, and the communities we serve.

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Innovation, Artificial Intelligence, and the Bedside Nurse - DailyNurse

This week, leading AI lab OpenAI released their latest project: an AI that can play hide-and-seek. Its the latest example of how, with current machine learning techniques, a very simple setup can produce shockingly sophisticated results.

The AI agents play a very simple version of the game, where the seekers get points whenever the hiders are in their field of view. The hiders get a little time at the start to set up a hiding place and get points when theyve successfully hidden themselves; both sides can move objects around the playing field (like blocks, walls, and ramps) for an advantage.

The results from this simple setup were quite impressive. Over the course of 481 million games of hide-and-seek, the AI seemed to develop strategies and counterstrategies, and the AI agents moved from running around at random to coordinating with their allies to make complicated strategies work. (Along the way, they showed off their ability to break the game physics in unexpected ways, too; more on that below.)

Its the latest example of how much can be done with a simple AI technique called reinforcement learning, where AI systems get rewards for desired behavior and are set loose to learn, over millions of games, the best way to maximize their rewards.

Reinforcement learning is incredibly simple, but the strategic behavior it produces isnt simple at all. Researchers have in the past leveraged reinforcement learning among other techniques to build AI systems that can play complex wartime strategy games, and some researchers think that highly sophisticated systems could be built just with reinforcement learning. This simple game of hide-and-seek makes for a great example of how reinforcement learning works in action and how simple instructions produce shockingly intelligent behavior. AI capabilities are continuing to march forward, for better or for worse.

You can watch the whole video here, or check out these highlights.

It may have taken a few million games of hide-and-seek, but eventually the AI agents figured out the basics of the game: chasing one another around the map.

AI agents have the ability to lock blocks in place. Only the team that locked a block can unlock it. After millions of games of practice, the AI agents learned to build a shelter out of the available blocks; you can see them doing that here. In the shelter, the seeker agents cant find them, so this is a win for the hiders at least until someone comes up with a new idea.

Millions of generations later, the seekers have figured out how to handle this behavior by the hiders: they can drag a ramp over, climb the ramp, and find the hiders.

After a while, the hiders learned a counterattack: they could freeze the ramps in place so the seekers couldnt move them. OpenAIs team notes that they thought this would be the end of the game, but they were wrong.

Eventually, seekers learned to push a box over to the frozen ramps, climb onto the box, and surf it over to the shelter where they can once again find the hiders.

Theres an obvious counterstrategy for the hiders here: freezing everything around so the seekers have no tools to work with. Indeed, thats what they learn how to do.

Thats how a game of hide-and-seek between AI agents with millions of games of experience goes. The interesting thing here is that none of the behavior on display was directly taught or even directly rewarded. Agents only get rewards when they win the game. But that simple incentive was enough to encourage lots of creative in-game behavior.

Many AI researchers think that reinforcement learning can be used to solve complicated tasks with real-world implications, too. The way powerful strategic decision-making emerges from simple instructions is promising but its also concerning. Solving problems with reinforcement learning leads, as weve seen, to lots of unexpected behavior charming in a game of hide-and-seek, but potentially alarming in a drug meant to treat cancer (if the unintended behavior causes life-threatening complications) or an algorithm meant to improve power plant output (if the AI arranges to exploit some obscure condition in its goals rather than simply provide consistent power).

Thats the hazardous flip side of techniques like reinforcement learning. On the one hand, theyre powerful techniques that can produce advanced behavior from a simple starting point. On the other hand, theyre powerful techniques that can produce unexpected and sometimes undesired advanced behavior from a simple starting point.

As AI systems grow more powerful, we need to give careful consideration to how to ensure they do what we want.

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An AI learned to play hide-and-seek. The strategies it came up with were astounding. - Vox.com