Sergey Tarasov - stock.adobe.com

In the continuing theme of higher level tools to improve developing useful applications, today well visit feature engineering in a changing environment. Artificial intelligence (AI) is increasingly used to analyze data, and deep learning (DL) is one of the more complex aspects of AI. In multiple forums, Ive discussed the need to move past heavy reliance on not just pure coding, but even past the basic frameworks discussed by DL programmers. One of the keys to the complexity is figuring out the right data attributes, or features, which matter to any system. Its even more important in DL, both because of larger data sets and due to the less transparent nature to the inference engine over procedural code. As tricky as that is the first time, it needs to be a repeatable process, as environments change, and systems must change with them.

Defining the initial feature set is important, but its not the end of the game. While many people focus on DLs ability to change results based on more data, that still means the use of the same features. For instance, the features are fairly well known radiology. Its gaining more examples for training that matters, to see the variation of how those features appear. However, what is theres a new tumor? There might be a new feature that needs to be added to the mix. With supervised systems, thats easy to modify because you can provide labeled images with the features and the system can be retrained.

However, what about consumer taste? Features are defined, then the deep learning system looks for relationships between the different defined features and provides analysis. However, fashion changes over time. Imagine, for instance, a system defined when all pants had pleats. The question of whether or not pants should have pleats isnt an issue, so the designers did not train the system to analyze the existence of pleats. While the feature might be defined in the full data set, for performance issues the feature was not engineered into the engine.

Suddenly, theres a change. People start buying pants without pleats. That becomes something that consumers want. While that might be in the full dataset, the inference engine is not evaluating that variable because it is not a defined feature. The environment has changed. How can that be recognized, and the DL system changed?

SparkBeyond is a company working to address the problem. While the product works with initial feature engineering, the key advantage is that it helps with DevOps and other processes to work to keep DL driven applications current in changing environments.

What the companys platform does is analyze the base data being used by the DL systems. It is not AI itself, but leverages random forests (RF). This technique is a way of running multiple tests with different parameters. This is helped by the advances of cloud technologies and the ability to scale-out to multiple servers. Large numbers of decision trees can be analyzed, with new patterns being seen. The RF is one of the ways that machine learning has moved past a pure AI definition, as it can create insight far faster than other methods, identifying new classifications and relationships in large data sets.

The complexities of consumer behavior, and that of financial and other markets, is far more complex than that of pleats v no-pleats, its important to recognize and adapt to change as fast as possible. Changing environments are critical to analysis, said Mike Sterling, Director of Impact Management, SparkBeyond. Generating large volumes of hypotheses and models, and them testing them, is critical to identifying those changes in order to adapt deep learning systems to remain accurate in those environments.

Artificial intelligence does not exist on its own. It is a technology that fits into a larger solution to address a business issue. No market is stagnant while remaining relevant. How and when to update deep learning systems, as they are used in more and more places, is important. The ability to analyze the data sets is critical, both for initial feature engineering and as an ongoing process to keep the systems relevant and accurate.

I see this as one feature, if you will, of what will eventually become development suites similar to 4GL development in the 90s. It will take a few more years, but this step to incorporate more tools into the deep learning environment

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Deep Learning AI Needs Tools To Adapt To Changes In The Data Environment - Forbes

Since February of last year, tens of thousands of patients hospitalized at one of Minnesotas largest health systems have had their discharge planning decisions informed with help from an artificial intelligence model. But few if any of those patients has any idea about the AI involved in their care.

Thats because frontline clinicians at M Health Fairview generally dont mention the AI whirring behind the scenes in their conversations with patients.

At a growing number of prominent hospitals and clinics around the country, clinicians are turning to AI-powered decision support tools many of them unproven to help predict whether hospitalized patients are likely to develop complications or deteriorate, whether theyre at risk of readmission, and whether theyre likely to die soon. But these patients and their family members are often not informed about or asked to consent to the use of these tools in their care, a STAT examination has found.

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The result: Machines that are completely invisible to patients are increasingly guiding decision-making in the clinic.

Hospitals and clinicians are operating under the assumption that you do not disclose, and thats not really something that has been defended or really thought about, Harvard Law School professor Glenn Cohen said. Cohen is the author of one of only a few articles examining the issue, which has received surprisingly scant attention in the medical literature even as research about AI and machine learning proliferates.

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In some cases, theres little room for harm: Patients may not need to know about an AI system thats nudging their doctor to move up an MRI scan by a day, like the one deployed by M Health Fairview, or to be more thoughtful, such as with algorithms meant to encourage clinicians to broach end-of-life conversations. But in other cases, lack of disclosure means that patients may never know what happened if an AI model makes a faulty recommendation that is part of the reason they are denied needed care or undergo an unnecessary, costly, or even harmful intervention.

Thats a real risk, because some of these AI models are fraught with bias, and even those that have been demonstrated to be accurate largely havent yet been shown to improve patient outcomes. Some hospitals dont share data on how well the systems work, justifying the decision on the grounds that they are not conducting research. But that means that patients are not only being denied information about whether the tools are being used in their care, but also about whether the tools are actually helping them.

The decision not to mention these systems to patients is the product of an emerging consensus among doctors, hospital executives, developers, and system architects, who see little value but plenty of downside in raising the subject.

They worry that bringing up AI will derail clinicians conversations with patients, diverting time and attention away from actionable steps that patients can take to improve their health and quality of life. Doctors also emphasize that they, not the AI, make the decisions about care. An AI systems recommendation, after all, is just one of many factors that clinicians take into account before making a decision about a patients care, and it would be absurd to detail every single guideline, protocol, and data source that gets considered, they say.

Internist Karyn Baum, whos leading M Health Fairviews rollout of the tool, said she doesnt bring up the AI to her patients in the same way that I wouldnt say that the X-ray has decided that youre ready to go home. She said she would never tell a fellow clinician not to mention the model to a patient, but in practice, her colleagues generally dont bring it up either.

Four of the health systems 13 hospitals have now rolled out the hospital discharge planning tool, which was developed by the Silicon Valley AI company Qventus. The model is designed to identify hospitalized patients who are likely to be clinically ready to go home soon and flag steps that might be needed to make that happen, such as scheduling a necessary physical therapy appointment.

Clinicians consult the tool during their daily morning huddle, gathering around a computer to peer at a dashboard of hospitalized patients, estimated discharge dates, and barriers that could prevent that from occurring on schedule. A screenshot of the tool provided by Qventus lists a hypothetical 76-year-old patient, N. Griffin, who is scheduled to leave the hospital on a Tuesday but the tool prompts clinicians to consider that he might be ready to go home Monday, if he can be squeezed in for an MRI scan by Saturday.

Baum said she sees the system as a tool to help me make a better decision just like a screening tool for sepsis, or a CT scan, or a lab value but its not going to take the place of that decision, she said. To her, it doesnt make sense to mention to patients. If she did, Baum said, she could end up in a lengthy discussion with patients curious about how the algorithm was created.

That could take valuable time away from the medical and logistical specifics that Baum prefers to spend time talking about with patients flagged by the Qventus tool. Among the questions she brings up with them: How are the patients vital signs and lab test results looking? Does the patient have a ride home? How about a flight of stairs to climb when they get there, or a plan for getting help if they fall?

Some doctors worry that while well-intentioned, the decision to withhold mention of these AI systems could backfire.

I think that patients will find out that we are using these approaches, in part because people are writing news stories like this one about the fact that people are using them, said Justin Sanders, a palliative care physician at Dana-Farber Cancer Institute and Brigham and Womens Hospital in Boston. It has the potential to become an unnecessary distraction and undermine trust in what were trying to do in ways that are probably avoidable.

Patients themselves are typically excluded from the decision-making process about disclosure. STAT asked four patients who have been hospitalized with serious medical conditions kidney disease, metastatic cancer, and sepsis whether theyd want to be told if an AI-powered decision support tool were used in their care. They expressed a range of views: Three said they wouldnt want to know if their doctor was being advised by such a tool. But a fourth patient spoke out forcefully in favor of disclosure.

This issue of transparency and upfront communication must be insisted upon by patients, said Paul Conway, a 55-year-old policy professional who has been on dialysis and received a kidney transplant, both consequences of managing kidney disease since he was a teenager.

The AI-powered decision support tools being introduced in clinical care are often novel and unproven but does their rollout constitute research?

Many hospitals believe the answer is no, and theyre using that distinction as justification for the decision not to inform patients about the use of these tools in their care. As some health systems see it, these algorithms are tools being deployed as part of routine clinical care to make hospitals more efficient. In their view, patients consent to the use of the algorithms by virtue of being admitted to the hospital.

At UCLA Health, for example, clinicians use a neural network to pinpoint primary care patients at risk of being hospitalized or frequently visiting the emergency room in the next year. Patients are not made aware of the tool because it is considered a part of the health systems quality improvement efforts, according to Mohammed Mahbouba, who spoke to STAT in February when he was UCLA Healths chief data officer. (He has since left the health system.)

This is in the context of clinical operations, Mahbouba said. Its not a research project.

Oregon Health and Science University uses a regression-powered algorithm to monitor the majority of its adult hospital patients for signs of sepsis. The tool is not disclosed to patients because it is considered part of hospital operations.

This is meant for operational care, it is not meant for research. So similar to how youd have a patient aware of the fact that were collecting their vital sign information, its a part of clinical care. Thats why its considered appropriate, said Abhijit Pandit, OHSUs chief technology and data officer.

But there is no clear line that neatly separates medical research from hospital operations or quality control, said Pilar Ossorio, a professor of law and bioethics at the University of Wisconsin-Madison. And researchers and bioethicists often disagree on what constitutes one or the other.

This has been a huge issue: Where is that line between quality control, operational control, and research? Theres no widespread agreement, Ossorio said.

To be sure, there are plenty of contexts in which hospitals deploying AI-powered decision support tools are getting patients explicit consent to use them. Some do so in the context of clinical trials, while others ask permission as part of routine clinical operations.

At Parkland Hospital in Dallas, where the orthopedics department has a tool designed to predict whether a patient will die in the next 48 hours, clinicians inform patients about the tool and ask them to sign onto its use.

Based on the agreement we have, we have to have patient consent explaining why were using this, how were using it, how well use it to connect them to the right services, etc., said Vikas Chowdhry, the chief analytics and information officer for a nonprofit innovation center incubated out of Parkland Health System in Dallas.

Hospitals often navigate those decisions internally, since manufacturers of AI systems sold to hospitals and clinics generally dont make recommendations to their customers about what, if anything, frontline clinicians should say to patients.

Jvion a Georgia-based health care AI company that markets a tool that assesses readmission risk in hospitalized patients and suggests interventions to prevent another hospital stay encourages the handful of hospitals deploying its model to exercise their own discretion about whether and how to discuss it with patients. But in practice, the AI system usually doesnt get brought up in these conversations, according to John Frownfelter, a physician who serves as Jvions chief medical information officer.

Since the judgment is left in the hands of the clinicians, its almost irrelevant, Frownfelter said.

When patients are given an unproven drug, the protocol is straightforward: They must explicitly consent to enroll in a clinical study authorized by the Food and Drug Administration and monitored by an institutional review board. And a researcher must inform them about the potential risks and benefits of taking the medication.

Thats not how it works with AI systems being used for decision support in the clinic. These tools arent treatments or fully automated diagnostic tools. They also dont directly determine what kind of therapy a patient may receive all of which would make them subject to more stringent regulatory oversight.

Developers of AI-powered decision support tools generally dont seek approval from the FDA, in part because the 21st Century Cures Act, which was signed into law in 2016, was interpreted as taking most medical advisory tools out of the FDAs jurisdiction. (That could change: In guidelines released last fall, the agency said it intends to focus its oversight powers on AI decision-support products meant to guide treatment of serious or critical conditions, but whose rationale cannot be independently evaluated by doctors a definition that lines up with many of the AI models that patients arent being informed about.)

The result, for now, is that disclosure around AI-powered decision support tools falls into a regulatory gray zone and that means the hospitals rolling them out often lack incentive to seek informed consent from patients.

A lot of people justifiably think there are many quality-control activities that health care systems should be doing that involve gathering data, Wisconsins Ossorio said. And they say it would be burdensome and confusing to patients to get consent for every one of those activities that touch on their data.

In contrast to the AI-powered decision support tools, there are a few commonly used algorithms subject to the regulation laid out by the Cures Act, such as the type behind the genetic tests that clinicians use to chart a course of treatment for a cancer patient. But in those cases, the genetic test is extremely influential in determining what kind of therapy or drug a patient may receive. Conversely, theres no similarly clear link between an algorithm designed to predict whether a patient may be readmitted to the hospital and the way theyll be treated if and when that occurs.

If it were me, Id say just file for institutional review board approval and either get consent or justify why you could waive it.

Pilar Ossorio, professor of law and bioethics, University of Wisconsin-Madison

Still, Ossorio would support an ultra-cautious approach: I do think people throw a lot of things into the operations bucket, and if it were me, Id say just file for institutional review board approval and either get consent or justify why you could waive it.

Further complicating matters is the lack of publicly disclosed data showing whether and how well some of the algorithms work, as well as their overall impact on patients. The public doesnt know whether OHSUs sepsis-prediction algorithm actually predicts sepsis, nor whether UCLAs admissions tool actually predicts admissions.

Some AI-powered decision support tools are supported by early data presented at conferences and published in journals, and several developers say theyre in the process of sharing results: Jvion, for example, has submitted to a journal for publication a study that showed a 26% reduction in readmissions when its readmissions risk tool was deployed; that paper is currently in review, according to Jvions Frownfelter.

But asked by STAT for data on their tools impact on patient care, several hospital executives declined or said they hadnt completed their evaluations.

A spokesperson from UCLA said it had yet to complete an assessment of the performance of its admissions algorithm.

Before you use a tool to do medical decision-making, you should do the research.

Pilar Ossorio, professor of law and bioethics, University of Wisconsin-Madison

A spokesperson from OHSU said that according to its latest report, run before the Covid-19 pandemic began in March, its sepsis algorithm had been used on 18,000 patients, of which it had flagged 1,659 patients as at-risk with nurses indicating concern for 210 of them. He added that the tools impact on patients as measured by hospital death rates and length of time spent in the facility was inconclusive.

Its disturbing that theyre deploying these tools without having the kind of information that they should have, said Wisconsins Ossorio. Before you use a tool to do medical decision-making, you should do the research.

Ossorio said it may be the case that these tools are merely being used as an additional data point and not to make decisions. But if health systems dont disclose data showing how the tools are being used, theres no way to know how heavily clinicians may be leaning on them.

They always say these tools are meant to be used in combination with clinical data and its up to the clinician to make the final decision. But what happens if we learn the algorithm is relied upon over and above all other kinds of information? she said.

There are countless advocacy groups representing a wide range of patients, but no organization exists to speak for those whove unknowingly had AI systems involved in their care. They have no way, after all, of even identifying themselves as part of a common community.

STAT was unable to identify any patients who learned after the fact that their care had been guided by an undisclosed AI model, but asked several patients how theyd feel, hypothetically, about an AI system being used in their care without their knowledge.

Conway, the patient with kidney disease, maintained that he would want to know. He also dismissed the concern raised by some physicians that mentioning AI would derail a conversation. Woe to the professional that as you introduce a topic, a patient might actually ask questions and you have to answer them, he said.

Other patients, however, said that while they welcomed the use of AI and other innovations in their care, they wouldnt expect or even want their doctor to mention it. They likened it to not wanting to be privy to numbers around their prognosis, such as how much time they might expect to have left, or how many patients with their disease are still alive after five years.

Any of those statistics or algorithms are not going to change how you confront your disease so why burden yourself with them, is my philosophy, said Stacy Hurt, a patient advocate from Pittsburgh who received a diagnosis of metastatic colorectal cancer in 2014, on her 44th birthday, when she was working as an executive at a pharmaceutical company. (She is now doing well and is approaching five years with no evidence of disease.)

Katy Grainger, who lost the lower half of both legs and seven fingertips to sepsis, said she would have supported her care team using an algorithm like OHSUs sepsis model, so long as her clinicians didnt rely on it too heavily. She said she also would not have wanted to be informed that the tool was being used.

I dont monitor how doctors do their jobs. I just trust that theyre doing it well.

Katy Grainger, patient who developed sepsis

I dont monitor how doctors do their jobs. I just trust that theyre doing it well, she said. I have to believe that Im not a doctor and I cant control what they do.

Still, Grainger expressed some reservations about the tool, including the idea that it may have failed to identify her. At 52, Grainger was healthy and fairly young when she developed sepsis. She had been sick for days and visited an urgent care clinic, which gave her antibiotics for what they thought was a basic bacterial infection, but which quickly progressed to a serious case of sepsis.

I would be worried that [the algorithm] could have missed me. I was young well, 52 healthy, in some of the best shape of my life, eating really well, and then boom, Grainger said.

Dana Deighton, a marketing professional from Virginia, suspects that if an algorithm scanned her data back in 2012, it would have made a dire prediction about her life expectancy: She had just been diagnosed with metastatic esophageal cancer at age 43, after all. But she probably wouldnt have wanted to know that at such a tender and sensitive time.

If a physician brought up AI when you are looking for a warmer, more personal touch, it might actually have the opposite and worse effect, Deighton said. (Shes doing well now her scans have turned up no evidence of disease since 2015.)

Harvards Cohen said he wants to see hospital systems, clinicians, and AI manufacturers come together for a thoughtful discussion around whether they should be disclosing the use of these tools to patients and if were not doing that, then the question is why arent we telling them about this when we tell them about a lot of other things, he said.

Cohen said he worries that uptake and trust in AI and machine learning could plummet if patients were to find out, after the fact, that theres a rash of this being used without anyone ever telling them.

Thats a scary thing, he said, if you think this is the way the future is going to go.

This is part of a yearlong series of articles exploring the use of artificial intelligence in health care that is partly funded by a grant from the Commonwealth Fund.

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An invisible hand: Patients aren't being told about the AI systems advising their care - STAT

Researchers have shared data suggesting drones and artificial intelligence (AI) could save the declining population of farmland birds. The drones use thermal cameras to detect the bird nests for later analysis by an AI system.

The researchers flew a drone equipped with a thermal camera over agricultural fields and took photos continuously. The images were then fed into an AI-backed algorithm to detect and point out bird nests in the field. A human can then be alerted to relocate the nest before heavy machinery comes through, threatening to destroy the nest and its occupants.

The team has been testing out this new system in Southern Finland near the University of Helsinkis Lammi Biological Station. The test subject is the Northern lapwing bird.

Heres Andrea Santangeli, an Academy of Finland fellow at the Finnish Museum of Natural History Luomus, University of Helsinki:

We have been involved in conservation of ground-nesting farmland birds for years, and realized how difficult it is to locate nests on the ground. At least at high latitudes, the temperature of these nests is typically higher than that of the surrounding environment. Hence, we thought that thermal cameras could assist. A small pilot study indicated that thermal vision is hampered by vegetation and objects on the ground. Therefore to make this an efficient system, we thought that the camera could be flown using a drone, and artificial intelligence could help to analyze the resulting thermal images. We show that this works. However, the system performed best under cloudy and cold conditions, and on even grounds.

What do you think of drones and AI being used to save farmland birds from possible extinction? Let us know your thoughts in the comments below.

Photo: Saffu

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Drones and AI could save farmland birds from extinction - DroneDJ

A joint effort to help companies harness edge computing and a low latency network with AI capabilities for real-time insights.

Image: iStockphoto/LHG

By combining IBM's proven success in AI, with its AI data analytics tool Watson serving a host of industries, and Verizon's long standing role in providing wirelessas well as its recent moves to accelerate the delivery of 5Gthe "Fourth Industrial Revolution" is here, according to a statement released Thursday. (The Fourth Industrial Revolution, according to TechRepublic's interview with Murat Snmez, director of the World Economic Forum, is "simultaneous development across artificial intelligence, drones, autonomous vehicles, gene editing, new materials, and 3D printing.")

The joint effort is intended to let industrial enterprises harness edge computing (which many companies still struggle with implementing, largely due to low network speeds) and a low latency network with AI capabilities to help deliver real-time insights to companies, per the announcement. The speed of 5G has many benefits for the enterprise, including industrial automation, enhanced AI deployment, and improved use of IoT. (In May, Verizon announced the launch of a new lab to help spur 5G development, as TechRepublic previously reported.)

As industries race to pull actionable data to remain efficient and productive, the collaboration is positioned to deliver "mobile asset tracking and management solutions to help enterprises improve operations, optimize production quality, and help clients enhance worker safety," according to the release.

The partnership will harness Verizon's 5G Ultra Wideband 5G networkwhich rolled out in 30 cities across the US in 2019its Multi-access Edge Computing, its ThingSpace IoT Platform and Critical Asset Sensor solution (CAS), together with IBM's Maximo Monitor with IBM Watson and advanced analytics, the release states, which can help enterprises highlight and address system issues and monitor asset health.

Companies are expected to gain real-time cognitive automation as a result of this partnership, which may include locating multiple devices at an industrial location. The 5G network is predicted to help organizations manage multiple devices in real time, which could have implications in robotics, video analytics, and plant automation, for instance, according to the release.

SEE: Future of 5G: Projections, rollouts, use cases, and more (free PDF) (TechRepublic)

"The industrial sector is undergoing unprecedented transformation as companies begin to return to full-scale operations, aided by new technology to help reduce costs and increase productivity," said Bob Lord, senior vice president, cognitive applications, blockchain and ecosystems, IBM, in the release. "Through this collaboration, we plan to build upon our longstanding relationship with Verizon to help industrial enterprises capitalize on joint solutions that are designed to be multicloud ready, secured and scalable, from the data center all the way out to the enterprise edge."

The partners said they planned to team up on "worker safety, predictive maintenance, product quality and production automation," the release stated.

"This collaboration is all about enabling the future of industry in the Fourth Industrial Revolution," said Tami Erwin, CEO, Verizon Business, in the release. "Combining the high speed and low latency of Verizon's 5G UWB Network and MEC capabilities with IBM's expertise in enterprise-grade AI and production automation can provide industrial innovation on a massive scale and can help companies increase automation, minimize waste, lower costs, and offer their own clients a better response time and customer experience."

5G networks and devices, mobile security, remote support, and the latest about phones, tablets, and apps are some of the topics we'll cover. Delivered Tuesdays and Fridays

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IBM and Verizon Business collaborate to merge AI computing with 5G networks for the enterprise - TechRepublic

The pharmaceutical business is perhaps the only industry on the planet, where to get the product from idea to market the company needs to spend about a decade, several billion dollars, and there is about 90% chance of failure. It is very different from the IT business, where only the paranoid survive but a business where executives need to plan decades ahead and execute. So when the revolution in artificial intelligence fueled by credible advances in deep learning hit in 2013-2014, the pharmaceutical industry executives got interested but did not immediately jump on the bandwagon. Many pharmaceutical companies started investing heavily in internal data science R&D but without a coordinated strategy it looked more like re-branding exercise with the many heads of data science, digital, and AI in one organization and often in one department. And while some of the pharmaceutical companies invested in AI startups no sizable acquisitions were made to date. Most discussions with AI startups started with show me a clinical asset in Phase III where you identified a target and generated a molecule using AI? or how are you different from a myriad of other AI startups? often coming from the newly-minted heads of data science strategy who, in theory, need to know the market.

However, some of the pharmaceutical companies managed to demonstrate very impressive results in the individual segments of drug discovery and development. For example, around 2018 AstraZeneca started publishing in generative chemistry and by 2019 published several impressive papers that were noticed by the community. Several other pharmaceutical companies demonstrated impressive internal modules and Eli Lilly built an impressive AI-powered robotics lab in cooperation with a startup.

However, it was not possible to get a comprehensive overview and comparison of the major pharmaceutical companies that claimed to be doing AI research and utilizing big data in preclinical and clinical development until now. On June 15th, one article titled The upside of being a digital pharma player got accepted and quietly went online in a reputable peer-reviewed industry journal Drug Discovery Today. I got notified about the article by Google Scholar because it referenced several of our papers. I was about to discard the article as just another industry perspective but then I looked at the author list and saw a group of heavy-hitting academics, industry executives, and consultants: Alexander Schuhmacher from Reutlingen University, Alexander Gatto from Sony, Markus Hinder from Novartis, Michael Kuss from PricewaterhouseCoopers, and Oliver Gassmann from University of St. Gallen. Upon a closer look it turned out to be not a perspective but a comprehensive research study with a head-to-head comparison of the pharmaceutical companies by their efforts in AI in research and development.

The study compared the pharmaceutical companies by the internal AI R&D projects, partnerships with AI startups, investments in AI startups and R&D alliances and consortiums between 2014 and 2018. It also compared the pharmaceutical companies by the number of scientific publications from 2014 and 2019 segmented into discovery, development, and others showing the clear leadership of Novartis in internal efforts and AstraZeneca in publications.

Overview of AI Related Activities 2014-2018 by Big Pharma Player Modified from Schuhmacher et al, ... [+] The upside of being a digital pharma player (2020), Drug Discovery Today

Before this study came out, to the industry insiders performing regular literature reviews it did feel like AstraZeneca was publishing more than any other pharmaceutical company. Only in 2019 AstraZeneca scientists published about 1,300 scientific papers. It also also felt that Bayer had a few nice papers. The highest number of publications across all segments was 65. For reference, a startup like Insilico Medicine published about 100 papers and about 30 patents in the same period not counting AI conference papers. Several other startups also did quite well in that area and it would be great to see similar analysis.

Number of scientific publications in AI by the pharmaceutical companies, 2014-2019, Modified from ... [+] Schuhmacher et al, The upside of being a digital pharma player (2020), Drug Discovery Today

I posted a screenshot of the study on LinkedIn and almost immediately the postwas viewed about 20,000 times primarily by the colleagues from the pharmaceutical industry. Surprisingly, very few of the viewers liked it. I suspect that many of them were quite disappointed to see that on the grand scheme of things the industry itself is still in its infancy. The study made it clear that there are many benefits of being a digital pharma player but we are still early in the process.

The authors of the study certainly deserve to be referred to as industry experts in the pharmaceutical AI R&D as they did a gargantuan amount of work to compile the three relatively simple figures in the study and at the moment no other study like that exists.

To learn more about the study, I wrote to the authors and asked them a few questions about the study and about their vision of the future of the pharmaceutical industry:

1. Looking under the hood of the top 21 big pharmaceutical companies and analyzing their activities in digital and AI is a gargantuan piece of work. Many analysts are trying to do the same thing with little success. How long did it take you and how did you manage to do it?

Gassmann:Indeed, it was a big piece of work. Much is publicly available, such as patents and scientific publications. In general, most valuable are interviews with executives in the pharma sector. Building up the reputation took for most of us more than 20 years.

Gatto: In addition, a key success factor was the interdisciplinary background of the authors including pharma strategy, R&D and AI competencies.

2. Did any of your findings surprise you?

Kuss: The findings were not surprising as such. But the early mature status with respect to the use of AI in pharma R&D seems to be a big challenge for the industry.

Schuhmacher: The future availability of low prized AI-applications in combination with faster and cheaper hardware will boost the trend of digitalization of pharma R&D. The immense need to increase R&D efficiency will do its part for the success of AI in pharma.

3. Did you see any conclusive case studies where AI dramatically outperformedhumans or any of the published work where AI replaced the need for experiments?

Gatto:We could identify several cases where we saw that there is the potential that AI might replace the need for experiments or outperforms humans. All in front a recent publication in Nature Biotechnology on de novo small-molecule design highlighting the huge potential of AI in drug discovery.

4. I am certain that some of the pharma CEOs, CFOs, and otherexecutives saw your paper by now. Did you get any comments? What was their initial reaction?

Schuhmacher: We did not get direct feedback yet, as the publication is brand new. In general, we noticed that pharma R&D executives have shown their interest in our recent work on virtualizing pharma R&D.

Gassmann: In addition, we can observe a slow change in pharma towards the digital side of health care. While 10 years ago many pharma managers could not believe that data based companies can really capture a larger part of the health care value chain, today it is more widely accepted that software eats the world, data change the pharma industry.

5. You even made a comparison of scientific publications between 2014 and 2019. My company published over 100 papers in that period, while the largest number for big pharma was 65 and some had zero publications. To me, it seems dramatically low. Why do you think this is the case?

Schuhmacher: It looks like that AI is still not part of the core strategies of some of the leading companies. And they still rely too much on the closed innovation paradigm: Publishing is not part of their revenue and R&D models. But this might change: Pharma companies need to be attractive to data scientists and other experts and need to show their excellence and competitiveness.

6. One of the major challenges in AI for drug discovery is intellectual property and many of the methods have blocking IP. In my opinion, one of the reasons why DeepMind was acquired early by Google was its strong IP portfolio. Did you look at the AI-related patents filed by these big pharmaceutical companies?

Gatto: Looking at the pure figures of AI-related patents reveals that there is a huge discrepancy between pharmaceutical companies and IT giants such as Google. But this pattern might change over time, when pharma is changing its R&D model and the way of how to exploit AI-related IP.

7. What do you think is going to happen in the next 1-2 years in this field?

Gassmann: 1-2 years is a short time for the pharma sector but AI will further come. Companies from the consumer electronics like Apple and data field like Google have already FDA registered wearables. Today those devices are still very unreliable but performance will increase fast. Chronical diseases such as Alzheimer, diabetes, or cancer will be the entry field for digital health interventions where longitudinal data create a lot of value. Pharma have to rethink the way they innovate and to start thinking in ecosystems.

Kuss: In our view, reimagine R&D as a crowdsourced ecosystem is the key for future success of pharma: pharmaceutical R&D will no longer be limited to predominantly internal value creation but will capitalize on a network of internal and external ideas, technologies (including AI), and resources.

8. Are you planning to update this report next year? And are you planning to add more pharmaceutical companies to the list?

Gassmann: This research should be just the start. For the next years we are planning to build up a collaborative center on pharma innovation research that will advance the insights on pharma and biotech R&D management in context of AI and other emerging technologies.

9. Can you tell me about the future directions for your research?

Schuhmacher: AI will have an immense impact on future R&D models and on the pharma R&D ecosystem as such. This together with other strategic and technological transformations will drive our research agenda for the coming months.

Kuss: Smart contracts based on distributed ledger technologies will play a key role in this change process.

For more information see:

The upside of being a digital pharma playerThe upside of being a digital pharma player (2020), Drug Discovery Today, DOI: 10.1016/j.drudis.2020.06.002

View original post here:

Deep Dive Into Big Pharma AI Productivity: One Study Shaking The Pharmaceutical Industry - Forbes

Last Chance: Register for Transform, VB's AI event of the year, hosted online July 15-17.

To kick off three days of AI panels, discussions, fireside chats, and networking at Transform 2020, VentureBeat presented the second annual AI Innovation Awards. Drawn both from our daily editorial coverage and the expertise, knowledge, and experience of our nominating committee members, these awards give us a chance to shine a light on the people and companies making an impact in AI.

Amid four nominees in each in our five categories Natural Language Processing/Understanding Innovation, Business Application Innovation, Computer Vision Innovation, AI for Good, and Startup Spotlight the winners have emerged.

Natural Language Processing/Understanding Innovation: StereoSet

Research continues to uncover bias in AI models; StereoSet is a dataset designed to measure discriminatory behaviors like racism and sexism in language models while ensuring that the models otherwise offer strong performance. Researchers Moin Nadeem, Anna Bethke, and Siva Reddy built StereoSet and have made it available to anyone who makes language models. They maintain a leaderboard to show how models like BERT and GPT-2 measure up.

Business Application Innovation: Jumbotail

Jumbotails technology revolutionizes traditional mom & pop stores in India, often known as kirana stores, by connecting them with brands and other high-quality product producers to help them emerge as modern convenience stores. Jumbotail does so without raising the cost to customers, by collecting and mining in real time millions of data points everyday. Thanks to its AI backend, Jumbotail became Indias leading online wholesale food and grocery marketplace, with a full stack that includes integrated supply chain and logistics, as well as an in-house fintech platform for payments and credit. The insights generated and tech developed around this new business model empowers producers and customers, and is poised to extend to other continents.

Computer Vision Innovation: Abeba Birhane and Dr. Vinay Prabhu

In their powerful work, Large image datasets: A pyrrhic win for computer vision?, researchers Abeba Birhane, PhD candidate at University College Dublin, and Dr. Vinay Prabhu, principal machine learning scientist at UnifyID, examined the problematic opacity, data collection ethics, labeling and classification, and consequences of large image datasets. These datasets, including ImageNet and MITs 80 Million Tiny Images, have been cited hundreds of times in research. This paper is under peer review, but already its resulted in MIT voluntarily and formally withdrawing the Tiny Images dataset on the grounds that it contains derogatory terms as categories as well as offensive images, and that the nature of the images in the dataset makes it infeasible to remedy the problems.

AI for Good: Dr. Timnit Gebru

Dr. Timnit Gebru continues to be one of the strongest voices in the AI community fighting racism, misogyny, and other biases not just in the actual technology, but within the wider community of AI researchers and practitioners. Shes the co-lead of Ethical AI at Google and cofounded Black in AI, a group dedicated to sharing ideas, fostering collaborations, and discussing initiatives to increase the presence of Black individuals in the field of AI. Her work includes Gender Shades, the landmark research exposing the racial bias in facial recognition systems, and Datasheets for Datasets, which aims to create a standardized process for adding documentation to datasets to increase transparency and accountability.

Startup Spotlight: Dr. Daniela Braga, DefinedCrowd Corp

DefinedCrowd Corp creates high-quality training data for enterprises AI and machine learning projects, including with voice recognition, natural language processing, and computer vision workflows. The company crowdsources data labeling and more from hundreds of thousands of paid contributors and passes the massive curation on to its enterprise customers. Customers include several Fortune 500 companies. The startups cofounder and CEO, Dr. Daniela Braga, has credentials in speech technology and crowdsourcing dating back nearly two decades, including nearly seven years at Microsoft that included work on Cortana. Shes led DefinedCrowd Corp through several rounds of funding most recently, a large $50.5 million round in May 2020.

We congratulate these winners, and all the nominees, for their important contributions to the field of AI!

Read more:

Announcing the AI Innovation Awards winners at Transform 2020 - VentureBeat

The fantasy of using Artificial Intelligence or AI in space sciences kick-started from the movie, 2001: A Space Odyssey. While it was a sci-fi concept, then, it is no longer a fiction anymore. Scientists around the world are using AI algorithms to predict the life of other planets in the solar system, detecting the presence of water, finding out the possibility of a Blackhole, or determining the orbital curve of a celestial object. According to NASA officials, AI could also aid in the search for life onalien planetsand the detection of nearby asteroids in space. What took years for earlier astronomers to discover can now be done in a shorter time duration by using machine learning models of AI. Now researchers from Princeton University have claimed to have found a way to predict if a planet will clash with another in its path.

In anew study, which is to be published in Proceedings of the National Academy of Sciences, scientists have described their AI model called Stability of Planetary Orbital Configurations Klassifier or SPOCK, for short.This model can predict the paths of exoplanets, and determine which ones will remain stable and which will crash into other worlds or stars, far more accurately and at greater scale than humans ever could. The name of the AI model is based on the beloved half-Vulcan and half-human first officer Mr. Spock of the starship Enterprise from the Star Trek series. The lead author of the study, Daniel Tamayo, a NASA Hubble Fellowship Program Sagan Fellow in astrophysical sciences at Princeton, explained in astatement, We called the model SPOCK partly because the model determines whether systems will live long and prosper.

Earlier astronomers struggled with the problem of orbital stability, including Newton. Though this led to mathematical revolutions, including calculus and chaos theory, no one has found a way to predict stable configurations theoretically. Tamayo and his colleagues realized that they could accelerate the process by combining simplified models of planets dynamical interactions with machine learning methods. This allows the elimination of vast swaths of unstable orbital configurations and frequency destabilization into a tangle of crossing orbits quickly. With SPOCK, one can determine the long-term stability of planetary configurations about 100,000 times faster.

Tamayo says, While SPOCK hasnt helped in understanding planetary stability, it will assist them in doing so with its ability to identify fast instabilities in compact systems reliably. This is most important whentrying to do stability constrained characterization. With the new AI model, we can understand the dynamics of orbiting planets, including those in our own Solar System. We cant categorically say This system will be OK, but that one will blow up soon, he added. The goal instead is, for a given system, to rule out all the unstable possibilities that would have already collided and couldnt exist at the present day.The co-authors of this research include graduate student Miles Cranmer and David Spergel, Princetons Charles A. Young Professor of Astronomy on the Class of 1897 Foundation, Emeritus.

Professor Michael Strauss, the chair of Princetons Department of Astrophysical Sciences,explainedthat with SPOCK, we can hope to understand in detail the full range of solar system architectures that nature allows. SPOCK is especially helpful for making sense of some of the faint, far-distant planetary systems recently spotted by the Kepler telescope, said Jessie Christiansen, an astrophysicist with the NASA Exoplanet Archive who was not involved in this research. Its hard to constrain their properties with our current instruments, she said. Are they rocky planets, ice giants, or gas giants? Or something new? This new tool will allow us to rule out potential planet compositions and configurations that would be dynamically unstableand it lets us do it more precisely and on a substantially larger scale than was previously available.

This interesting development in AI for planetary sciences comes after last years exciting news on how AI helped space scientists in various projects. In March 2019, astronomers at The University of Texas at Austin, in partnership with Google, used AI to uncover two more hidden planets in the Kepler space telescope archive (Keplers extended mission, called K2). There they used an AI algorithm that sifts through the data taken by Kepler to ferret out signals that were missed by traditional planet-hunting methods. This helped in the discovery of the planets K2-293b orbiting around a star 1,300 light-years away in the constellation Aquarius and planet K2-294b, revolving around a star 1,230 light-years away, also located in Aquarius. In November, AI discovered that Earth revolves around the Sun. This was possible because of physicist Renato Renner at the Swiss Federal Institute of Technology (ETH) in Zurich and his collaboratorswho designed a neural network model based on machine learning to help physicists to solve apparent contradictions in quantum mechanics.

Last month, NASA unveiled an AI system that helps find life on other planets in our solar system, especially Mars. The machine learningalgorithms of this AI system will help exploration devices analyze soil samples on Mars and return the most relevant data to NASA. Eventually, NASA aims to use the system in future missions to the moons of Jupiter and Saturn. At present, the AI system has now been trained to analyzehundreds of rock samples and thousands ofwavelengths of electromagnetic radiation with an accuracy of 94 percent.

Read this article:

Navigating the potential of Artificial Intelligence (AI) in Space Sciences - Analytics Insight

The unprecedented onset of a pandemic crisis such as COVID-19 has been instrumenting dominant alterations in the global growth trajectory of the Artificial Intelligence (AI) in Education Market. The event marks a catastrophic influence affecting myriad facets of the Artificial Intelligence (AI) in Education market in a multi-dimensional setting. The growth course that has been quite unabashed in the historical times, seems to have been struck suddenly in various unparalleled ways and means, which is therefore also affecting the normal growth prospects in the Artificial Intelligence (AI) in Education market. This thoughtfully compiled research report underpinning the impact of COVID-19 on the growth trajectory is therefore documented to encourage a planned rebound.

A thorough analytical review of the pertinent growth trends influencing the Artificial Intelligence (AI) in Education market has been demonstrated in the report. Adequate efforts have been directed to influence an unbiased and time-efficient market related decision amongst versatile market participants, striving to find a tight grip in the competition spectrum of the aforementioned Artificial Intelligence (AI) in Education market. The report also illustrates minute details in the Artificial Intelligence (AI) in Education market governing micro and macroeconomic factors that seem to have a dominant and long-term impact, directing the course of popular trends in the global Artificial Intelligence (AI) in Education market.

The study encompasses profiles of major companies operating in the Artificial Intelligence (AI) in Education Market. Key players profiled in the report includes:GoogleIBMPearsonMicrosoftAWSNuanceCognizantMetacogQuantum Adaptive LearningQueriumThird Space LearningAleksBlackboardBridgeUCarnegie LearningCenturyCogniiDreamBox LearningElemental PathFishtreeJellynoteJenzabarKnewtonLuilishuo

The report is rightly designed to present multidimensional information about the current and past market occurrences that tend to have a direct implication on the onward growth trajectory of the Artificial Intelligence (AI) in Education market.

The following sections of this versatile report on the Artificial Intelligence (AI) in Education market specifically shed light on popular industry trends encompassing both market drivers as well as dominant trends that systematically affect the growth trajectory visibly. The report is a holistic, ready-to-use compilation of all major events and developments that replicate growth in the Artificial Intelligence (AI) in Education market. Besides presenting notable insights on Artificial Intelligence (AI) in Education market factors comprising above determinants, the report further in its subsequent sections of this detailed research report on Artificial Intelligence (AI) in Education market states information on regional segmentation.

Access Complete Report @ https://www.orbismarketreports.com/global-artificial-intelligence-ai-in-education-market-growth-analysis-by-trends-and-forecast-2019-2025?utm_source=Pooja

By the product type, the market is primarily split into Machine Learning and Deep LearningNatural Language Processing

By the end-users/application, this report covers the following segments Virtual Facilitators and Learning EnvironmentsIntelligent Tutoring SystemsContent Delivery SystemsFraud and Risk Management

In the subsequent sections of the report, readers are also presented with versatile understanding about the current state of geographical overview, encompassing various regional hubs that consistently keep witnessing growth promoting market developments directed by market veterans, aiming for ample competitive advantage, such that their footing remains strong and steady despite the cut throat competition characterizing the aforementioned Artificial Intelligence (AI) in Education market. Each of the market players profiled in the report have been analyzed on the basis of their company and product portfolios, to make logical deductions.

Global Artificial Intelligence (AI) in Education Geographical Segmentation Includes: North America (U.S., Canada, Mexico) Europe (U.K., France, Germany, Spain, Italy, Central & Eastern Europe, CIS) Asia Pacific (China, Japan, South Korea, ASEAN, India, Rest of Asia Pacific) Latin America (Brazil, Rest of L.A.) Middle East and Africa (Turkey, GCC, Rest of Middle East)

Some Major TOC Points: Chapter 1. Report Overview Chapter 2. Global Growth Trends Chapter 3. Market Share by Key Players Chapter 4. Breakdown Data by Type and Application Chapter 5. Market by End Users/Application Chapter 6. COVID-19 Outbreak: Artificial Intelligence (AI) in Education Industry Impact Chapter 7. Opportunity Analysis in Covid-19 Crisis Chapter 9. Market Driving ForceAnd Many More

Continued

Research Methodology Includes:

The report systematically upholds the current state of dynamic segmentation of the Artificial Intelligence (AI) in Education market, highlighting major and revenue efficient market segments comprising application, type, technology, and the like that together coin lucrative business returns in the Artificial Intelligence (AI) in Education market.

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Target Audience:* Artificial Intelligence (AI) in Education Manufactures* Traders, Importers, and Exporters* Raw Material Suppliers and Distributors* Research and Consulting Firms* Government and Research Organizations* Associations and Industry Bodies

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Global Artificial Intelligence (AI) in Education Market Projected to Reach USD XX.XX billion by 2025- Google, IBM, Pearson, Microsoft, AWS, Nuance,...

GYANT, maker of an artificial intelligence-based virtual-healthcare assistant, has hauled in a $13.6 million Series A financing round. Wing Venture Capital headlined the raise, which also included support from Intermountain Ventures, Grazia Equity, Alpana Ventures, Techstars Ventures and Plug and Play Ventures.

WHAT IT DOES

A veteran of the Cedars-Sinai Tech Stars accelerator, GYANT's care-navigation business looks to serve as the "digital front door" for healthcare-provider organizations. The AI tool engages patients via chat to uncover their needs and direct them toward the services or tools they may need. GYANT's product can be added to websites, patient apps, or patient portals, and integrates with the organization's EHR systems.

HIMSS20 Digital

The startup appears to have gotten a leg up over the past year or so. GYANT said in its funding announcement that it's grown from three customers in July 2019 to 24 a year later, and saw wide deployment of an automated COVID-19 screener it developed in March.

WHAT IT'S FOR

GYANT said that the new funds will help further its tech platform and support greater interoperability for its customers.

The need for digital access and care navigation has never been greater, especially with healthcare inequities and disparities in the spotlight today," Stefan Behrens, CEO and cofounder, said in a statement. "This is the time for GYANT to continue growing and realize our vision of personalized patient experiences with digital navigation to the right, best possible care.

MARKET SNAPSHOT

GYANT isn't alone in deploying triage chatbots to overwhelmed healthcare providers. Ada Health, Buoy Health, Bright.md, Babylon Health and others have been racking up funds over the past few years and signing a growing number of deployment deals, ranging from carecoordination to automated delivery of care reminders. And as COVID-19 cases mount in the U.S., these technologies are increasingly being relied uponto ensure patients receive appropriate guidance.

ON THE RECORD

Intermountain Healthcare partnered with GYANT for screening and care navigation in the height of COVID-19 and the results speak for themselves, Dr. Mike Phillips, managing director and partner of Intermountain Ventures, said in a statement. Using GYANT led to a 30% decrease in call center volume, alleviating hospital capacity constraints and improving patient engagement. Our first-hand experience with GYANT and the value its market leading care navigation solution delivers drove our decision to invest.

Excerpt from:

GYANT hauls in $13.6M Series A for AI care coordination tool - MobiHealthNews

Commercialization giant Eversana has bulked up its data and analytics offering with the acquisition of HVH Precision Analytics from Havas Health & You (HH&Y) and Perspecta. The deal adds a range of data-fueled capabilities, including advanced machine learning and patient identification in rare and misdiagnosed disorders, to Eversanas expanding slate.

The deal comes as somewhat of a surprise, if only because HH&Y leadership had long touted the HVH unit as its secret weapon.

Eversana and HH&Y will, however, continue to work together, with details of what the companies characterized as an exclusive strategic partnership set to be disclosed within the next few weeks.

Were maintaining the relationship with Havas and will expand it, said Brigham Hyde, president, data and analytics at Eversana. He believes, for instance, that the market access/payer and value communications strengths of Eversana Engage pair well with HH&Ys expansive offerings.

Very often were the execution arm of the commercialization process, Hyde explained. A lot of time, the advice and guidance that Havas creates, we end up executing. Having a tighter tie there made a ton of sense. An HH&Y spokesperson did not immediately respond to an emailed request for comment.

The deal came together during the pandemic shutdown, with Eversana one of multiple bidders, Hyde said. After a host of phone calls and Zoom meetings, Hyde and HVH CEO Steve Costalas sorted many of the remaining details at a socially distanced meal in Princeton, NJ. Costalas will remain with the company, though his titleand those of other HVH leadershasnt been finalized. The HVH brand will be formally merged into Eversana before the end of the year.

Given the broad range of activities in which Eversana engages on behalf of its clients everything from running copay programs to servicing specialty pharmacies importing deeper data and analytics expertise is a no-brainer. At the core, what makes those services run well is data and analytics, Hyde said. This is a great building block for what were trying to become, as both a prediction-driven business and a digital business.

HVH Precision Analytics was formally debuted in early 2017 by the predecessor organizations of HH&Y (Havas Health) and Perspecta (Vencore). At the time, then-HVH chief operating officer Jeff Ceitlin noted that the units analytical rigor had been battle-tested, literally, in a different arena. If [Vencore] can use data and analytics to find bad guys in Afghanistan, they can use it to find [undiagnosed] patients, he told MM&M.

In the wake of the acquisition, HVHs 30 or so full-time employees will be integrated into Eversanas data and analytics unit, while its primary Wayne, PA, office will become the 31st outpost in the Eversana global network. The other three locations listed on HVHs website in New York, Boston and Hamilton Township, NJ are Havas network sites that hosted small HVH teams, according to an Eversana spokesperson. Eversana counts more than 2,700 employees around the globe.

Hyde joined Eversana in April. He arrived from Concerto HealthAI, a data and AI startup focused on oncology.

Originally posted here:

Eversana ups data and AI power with acquisition of HVH Precision Analytics - Agencies - MM&M - Medical Marketing and Media

[co-author: Adam Perkins, Trainee Solicitor]

On June 15, 2020, the Government of the United Kingdom issued a joint statement announcing the creation of the Global Partnership on Artificial Intelligence (GPAI) along with 14 other founding members, including the European Union and the United States of America.

As announced, GPAI is an international partnership that will aim to promote the responsible development and use of Artificial Intelligence (AI) in a human-centric manner. This means developing and deploying AI in a way that is consistent with human rights, fundamental freedoms and shared democratic values. GPAIs aim is to bridge the gap between theory and practice on AI by supporting cutting-edge research and applied activities on AI-related priorities.

The values which GPAI endorses reflect the core AI principles as promoted by the Organisation for Economic Co-operation and Development (OECD) in the May 2019 OECD Council Recommendation on AI. The OECD will be the host of GPAIs Secretariat in Paris, and GPAI will draw upon the OECDs international AI policy leadership. It is thought that this integration will strengthen the evidence base for policy aimed at responsible AI. In addition, GPAI has stated that it is looking forward to working with other interested countries and partners.

Centres of Expertise in Montreal and Paris will provide research and administrative support to GPAI, while the GPAI Secretariat will lend support to GPAIs governing bodies, consisting of a council and steering committee. GPAI will engage in scientific and technical work and analysis, bringing together experts within academia, industry and government to collaborate across the following four initial working groups:

The outlook of these working groups appears to reflect GPAIs recognition of the potential for AI to act as a catalyst for sustainable economic growth and development, providing that it can be done in an accountable, transparent and responsible manner.

GPAIs short term priority, however, is to investigate how AI can be used to help with the response to, and recovery from, COVID-19.

The first annual GPAI Multistakeholder Experts Group Plenary is planned to take place in December 2020.

The creation of GPAI is an exciting new step in the global effort to harvest the possibilities which AI offers in an ethical and responsible way, minimizing the risks to individuals rights and freedoms. We will be monitoring its progress.

Link:

Joint Statement on the Creation of the Global Partnership on Artificial Intelligence - JD Supra

Last Chance: Register for Transform, VB's AI event of the year, hosted online July 15-17.

When enterprise data software company Cloudera looked into using conversational AI to improve its customer support question-and-answer experience, it didnt want to go slow, said senior director of engineering Adam Warrington in a conversation at Transform 2020. When your company is new to conversational AI, conventional wisdom says you might gradually ease into it with a simple use case and an off-the-shelf chatbot that learns over time.

But Cloudera is a data company, which gives it a head start. We were kind of interested in how we could possibly use our own data sets and technologies that we had internally to do something a little bit more than just dipping our toes into the water, Warrington said. We were more interested in getting off-the-shelf chatbot software that was extensible through APIs, he added. Warrington said Cloudera already had an internally stored wealth of data in the form of customer interactions, support cases, community posts, and so on. The idea was to answer customer support questions with a high degree of accuracy without having to wait for the chatbot to acquire domain knowledge.

Because Cloudera maintained records again, this is a data company of past customer issues and solutions, it had its own corpus to feed the chatbot. In order to teach the chatbot, the company wanted to extract the semantic context of things like the back-and-forth chatter between a support person and customer, as well as the specifics of the actual problem being solved.

To ensure that they knew what was relevant, the Cloudera team relied on their own subject experts to manually label and classify the data set. The work can be a little bit tedious, as is the case with many machine learning projects, but you dont need in this particular case millions and millions of things categorized and labeled, Warrington said. He added that after about a week of work, they ended up with a labeled data set they could use for training and testing. And, Warrington said, they achieved their goal of 90% accuracy.

The company now had models that could understand which words and sentences within a given support case were technically relevant to that case. Then the models could extract the right solution from the best source, be it a knowledge base article, product documentation, community post, or what have you.

But the team needed to go a step further. Now theres the derivative problem downstream, which is [that] what we actually want to do is provide answers to the customers that are relevant to their problems. Its not just about understanding whats technically relevant and whats not, Warrington said. Here again, the team relied on subject matter experts specifically, support engineers to ensure customers were receiving the best solutions.

Warrington said that although Cloudera is currently using its subject matter experts internally, more data is coming in from real interactions. As this project continues to go on in the public space, we expect to get more signals from our customers that are actually using the chatbot, he said. And so well start to use those inputs, those signals, from our customers to really expand on our test sets and our training set, to improve the quality from where its at today.

Whats perhaps most surprising is the short time to market. From inception of the problem statement of trying to use our own data sets and our own technology to augment chatbot software to return relevant results based on customer problem descriptions this took under a month, Warrington said. Why so fast? It certainly helped that Cloudera has its data already set up in its own data lake. All of our processing capabilities already exist on top of this, so everything from analytics to operational databases to our machine learning systems and things like Spark were able to access these data sets through these different technologies.

More to the point, Warrington said in the course of researching chatbot software they could use, the team discovered they already had some pertinent models. They had previously built models to help their internal engineers more efficiently find and address customer support issues. It turns out when youre running all these machine learning projects on an architecture like this, you can share work that has been done in the past that you didnt necessarily expect to use in this way, Warrington noted. He also said the fact that they had a modern data structure, meaning the data was already unsiloed, was a huge advantage.

In addition to the wisdom of relying on subject matter experts, focusing on a specific problem or set of problems, and starting with data architectures that grant you agility, Warringtons advice is to keep things simple. As we grow and mature, this particular approach in this particular implementation we very well could go and explore more advanced techniques [and] more advanced models as we add more types of signals into the system, he said. But out of the gate, to hit the ground running, use something simple. We found that you can actually provide very useful results to the customers, very quickly, using these kinds of approaches.

Read the rest here:

Cloudera built its conversational AI chops by keeping things simple - VentureBeat

When it comes to advancements in artificial intelligence technology, China does have a lead in some places like spying on its own people and using facial recognition technology to identify political dissenters. But those are areas where the U.S. simply isn't pointing its investments in artificial intelligence, said director of the Joint Artificial Intelligence Center. Where it counts, the U.S. leads, he said.

"While it is true that the United States faces formidable technological competitors and challenging strategic environments, the reality is that the United States continues to lead in AI and its most important military applications," said Nand Mulchandani, during a briefing at the Pentagon.

The Joint Artificial Intelligence Center, which stood up in 2018, serves as the official focal point of the department's AI strategy.

China leads in some places, Mulchandani said. "China's military and police authorities undeniably have the world's most advanced capabilities, such as unregulated facial recognition for universal surveillance and control of their domestic population, trained on Chinese video gathered from their systems, and Chinese language text analysis for internet and media censorship."

The U.S. is capable of doing similar things, he said, but doesn't. It's against the law, and it's not in line with American values.

"Our constitution and privacy laws protect the rights of U.S. citizens, and how their data is collected and used," he said. "Therefore, we simply don't invest in building such universal surveillance and censorship systems."

The department does invest in systems that both enhance warfighter capability, for instance, and also help the military protect and serve the United States, including during the COVID-19 pandemic.

The Project Salus effort, for instance, which began in March of this year, puts artificial intelligence to work helping to predict shortages for things like water, medicine and supplies used in the COVID fight, said Mulchandani.

"This product was developed in direct work with [U.S. Northern Command] and the National Guard," he said. "They have obviously a very unique role to play in ensuring that resource shortages ... are harmonized across an area that's dealing with the disaster."

Mulchandani said what the Guard didn't have was predictive analytics on where such shortages might occur, or real-time analytics for supply and demand. Project Salus named for the Roman goddess of safety and well-being fills that role.

"We [now have] roughly about 40 to 50 different data streams coming into project Salus at the data platform layer," he said. "We have another 40 to 45 different AI models that are all running on top of the platform that allow for ... the Northcom operations team ... to actually get predictive analytics on where shortages and things will occur."

As an AI-enabled tool, he said, Project Salus can be used to predict traffic bottlenecks, hotel vacancies and the best military bases to stockpile food during the fallout from a damaging weather event.

As the department pursues joint all-domain command and control, or JADC2, the JAIC is working to build in the needed AI capabilities, Mulchandani.

"JADC2 is ... a collection of platforms that get stitched together and woven together[ effectively into] a platform," Mulchandani said. "The JAIC is spending a lot of time and resources focused on building the AI components on top of JADC2. So if you can imagine a command and control system that is current and the way it's configured today, our job and role is to actually build out the AI components both from a data, AI modeling and then training perspective and then deploying those."

When it comes to AI and weapons, Mulchandani said the department and JAIC are involved there too.

"We do have projects going on under joint warfighting, which are actually going into testing," he said. "They're very tactical-edge AI, is the way I describe it. And that work is going to be tested. It's very promising work. We're very excited about it."

While Mulchandani didn't mention specific projects, he did say that while much of the JAIC's AI work will go into weapons systems, none of those right now are going to be autonomous weapons systems. The concepts of a human-in-the-loop and full human control of weapons, he said, "are still absolutely valid."

See the original post here:

Where it Counts, U.S. Leads in Artificial Intelligence - Department of Defense

Before Dr. Matthew Fradkin was a pediatrician he played in a punk rock band. Aside from the years of training to become a physician, he said there were actually some similarities to the two namely the search for human connection.

They have the same core, the in-the-moment human connection is really important to both. Bringing back the human connection in medicine is where I see digital andAI advancing in helping to turn back the clock in terms of the patient provider experience, Fradkin, a pediatric and Swedish Primary Care and Providence St. Josephs, said during aHIMSS20 Digital event.

With more and more documentation piling up in the medical world, Fradkin said that connection with the patient is in jeopardy and so is provider burnout.

I want to find ways to make caregiving easier for our providers, and by finding ways for our providers to care for patients the way they want to in a large healthcare system, pushing towards standardization for population health advances. Or basically how do we prevent provider burnout using various digital tools and technology.

Physician burnout is associated with work, personal life balance, and organizational factors, he said.

The only thing I can really make an impact on are the work factors. Im probably not going to change a 50-year old primary care doctors personality, and Im not on the C-suite, so Im pretty sure I cant make structural change overnight so work factors for me is the obvious category to start addressing.

He decided to start with a notorious pain point in carethe documentation. He noted that, while EMRs provide doctors with a plethora of data points,they shoulder the bulk of the input burden. That was when he began to look at AI and machine learning scribes to help ease this issue.

We want ambient technology to provide an accurate note where providers dont change a thing in how they deal with patients in the clinical room or in their head, he said.

He started to work with the digital innovation team at his health system to look into pilots for fixing this issue.

Part of the digital innovation core of Swedish and Providence St. Joe's is the accelerated pilot process. This is a set framework that allows providers to investigate possible new technologies to help with their clinic experience, he said. This pilot allowed us to follow that framework and to evaluate possible vendors in a virtual arena, come up with KPIs or key performance indicators and streamlining the path through IT, security, legal or any other red tape that prevents pilots from occurring in large health system.

The tech that Fradkin decided to pilot was an AI-based medical scribe that is able to train and learn a providers individual style and preferences over time.

Initially, so the provider does not have to do heavy lifting in training the ML, there is a real-person reviewer offsite, reviewing what the ML is coming up with after a visit, and correcting it and making adjustments in the background based on my personal and organization-wide templates already in the system, he said.

The system lets providers choose how they would like to use it. Some just used it for the simple transaction notes. Others use the snip-it mode, where they say a phrase that helps the system choose a certain path. Lastly there was an ambient mode, which listens to the whole encounter and is able to do the bulk of the charting.

During and after the pilot I was able to go from seeing 16 to 17 patients a dayto 23, with a range of 22 to 30 with the entirety of the pilot where, for return on investment with the particular product we used, I would have to see one patient extra a week, four extra patients a month, to pay for the product, he said.

But Fradkin said that this type of system would do more than just boost the number of patients he could see.It would also help burnout.

Venturing into this new realm of AI-driven scribes is an exciting one, and one that needs to provide caregivers flexibility and responsivenessthe same factors parents expect from their providers in this new digital age of medicine, he said. Yes, there is a cost to this, never mind the fact that most of these solutions pay for themselves in the short run.But, if we have the same urgency to finding an answer to 'the cost of losing physician to burnout'from the EMRas we do to any other disease, we wouldnt even be having this discussion about cost. It makes care better for the providers and patients.

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Doctors are burdened by documentation, are AI scribes the answer? - MobiHealthNews

News Summary:

SAN JOSE, Calif., July 16, 2020 (GLOBE NEWSWIRE) -- Guavus, a pioneer in AI-based analytics for communications service providers (CSPs), today announced the launch of Guavus-IQ -- a comprehensive product portfolio that provides a unique multi-perspective analytics experience for CSPs.

Guavus-IQ delivers highly instrumented analytics insights to CSPs on how each subscriber is experiencing their network and services (bringing the outside perspective in) and how their network is impacting their subscribers (understanding how their internal operations are impacting their customers). This single, real-time outside-in/inside-out perspective helps operators identify subscriber behavioral patterns and better understand their operational environments. This enables them to increase revenue opportunities through data monetization and improved customer experience (CX), as well as reduce costs through automated, closed-loop actions.

In addition, Guavus-IQ has been designed to be operator-friendly for CSPs -- it doesnt require the operator to be a data science specialist or expert. It combines network and data science and leverages explainable AI to deliver easy-to-understand analytics insights to CSP users across the business at a significantly reduced cost.

The new Guavus-IQ products build on Guavus ten plus years of experience providing innovative analytics solutions focused exclusively on the needs of CSPs. The products are currently deployed in 8 of the top CSPs in Europe, Latin America, Asia-Pac and North America.

Big Data Doesnt Need to Come at a Big Cost

Guavus-IQ consists of two main product categories:

Just because data is big doesnt mean it cant be resource-efficient. The Guavus-IQ products leverage approximately 50% of the compute/processing-related hardware required by traditional analytics solutions through their use of advanced big data collection capabilities and real-time, in-memory stream processing edge analytics. This results in more powerful data collection from over 200 sources at half the cost.

Ops-IQ provides additional operational efficiencies through a combination of anomaly detection, fault correlation, and root cause analysis -- which not only lower OPEX but elevate CX. Ops-IQ fault analytics suppress more than 99.5% of alarms not associated with network incidents, and accurately predict incident-causing alarms by 93.9%. This significantly improves the Mean-Time-To-Response (MTTR) in a CSP Network Operations Center (NOC), saving more than $10 million a year in OPEX costs currently for a large service provider customer.

Service-IQ also plays a significant role in positively impacting CX and reducing costs. Service-IQ allows for flexible data reuse when it ingests new data, it ingests data once and then enables the reuse of that same data for additional use cases across both Service-IQ and Ops-IQ. This new level of efficiency saves operators time with ingest, a costly and complex part of the analytics process.

Because the data pipeline of previously ingested data can be automatically re-instantiated for use within Service-IQ or Ops-IQ, CSPs dont need to become big data experts in order to leverage the power and value of the data theyve collected. Instead, the Guavus-IQ products apply proven data science methods inside the integrated solutions to do the heavy lifting for the operator. This also allows analytics projects to be streamlined and shortened by more than 40-50%, as many organizations struggle not only with managing and deploying the infrastructure but also with gaining value in the early stage of analytics and AI experimentation.

Supporting Quotes:

In the world of 5G, IoT and now a global pandemic, were seeing an even greater need for operators to take advantage of AI and analytics to deal with increased network complexity, operational costs and subscriber demands for improved experience. To address these challenges, operators need to better understand network and subscriber behavior and be able to do so in real time.

These challenges can be tackled by utilizing big data collection, in-memory stream processing and AI-based analytics capabilities to ingest, correlate and analyze data (on premise and in the cloud) in real time from operators multivendor infrastructure. Insights generated can then be used to better serve operators needs across network, service, and marketing operations.Adaora Okeleke, Principal Analyst, Service Provider Operations and IT, Omdia

Weve seen a lot of excitement from the top CSPs worldwide in Guavus-IQ. Our customers plan to leverage the products for root cause analysis, subscriber behavior analysis, new personalized products, and IoT services, among other use cases. They like the fact that Guavus-IQ is easy to operate and its highly instrumented specifically for operators and their multivendor infrastructures versus traditional general-purpose enterprise platforms or homogeneous network-equipment-oriented solutions.Alexander Shevchenko, CEO of Guavus, a Thales company

Additional Resources:

About Guavus (a Thales company)Guavus is at the forefront of AI-based big data analytics and machine learning innovation, driving digital transformation at 6 of the 7 world's largest telecommunications providers. Using the Guavus-IQanalytics solutions, customers are able to analyze big data in real time and take decisive actions to lower costs, increase efficiencies, and dramatically improve the end-to-end customer experience all with the scale and security required bynext-gen 5G and IoT networks.

Guavus enables service providers to leverage applications for advanced network planning and operations, mobile traffic analytics, marketing, customer care, security and IoT. Discover more at http://www.guavus.com and follow us on Twitter and LinkedIn.

Media Contact:Laura StiffGuavus PR & Analyst Relations+1-408-827-1242laura.stiff@external.thalesgroup.com

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Guavus Unwraps New AI-based Analytics and Automation Products for CSPs - GlobeNewswire

An ER physician says doctors have to consider biological sex to properly care for their patients. Coffee can send some people to the bathroombut probably not for the reason weve been led to believe. AI-powered text generators can write realistic news stories, fueling concerns that the technology will encourage the spread of misinformation online.

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Join geneticist Kevin Folta and GLP editor Cameron English on this episode of Science Facts and Fallacies as they break down these latest news stories:

When physicians dont properly consider biological sex, patients are prescribed incorrect treatments and suffer entirely preventable consequences, says Alyson McGregor, an Associate Professor of Emergency Medicine at The Warren Alpert Medical School of Brown University.

The problem runs through our health care system, affecting millions of patients, and stems from the fact that doctors often prescribe multiple medications to female patients without recognizing female sex as an independent risk factor for serious drug interactions, McGregor notes. This occurs because women are more likely to have multiple physicians prescribing medications, each possibly unaware of all the relevant drugs unless the patient reports them.

Is there a way to correct the situation and prevent needless suffering?

Scientists and educators spend a considerable amount of time combating the spread of misinformation online, and their jobs may get much harder in the coming years as text generators powered by artificial intelligence become more widely used. These applications can perform word association, answer questions and, perhaps most importantly, comprehend related concepts.

The latest iteration of the technology developed by OpenAI was able to write 200-500 word sample news articles that were difficult to distinguish from news reports written by humans. There are some inherent risks in the technology, but AI-powered text generators are also poised to do a lot of good.

Its a common joke youve probably heard in your favorite movie or TV show: that first morning cup of coffee makes you poop. While it may not be as universal as implied by pop culture, this reaction to coffee is real. Caffeine might be one of the culprits. However, multiple (albeit small) studies show that coffee stimulates several physiological responses that can send you to the bathroom in short order.

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Kevin M. Folta is a professor in the Horticultural Sciences Department at the University of Florida. Follow Professor Folta on Twitter @kevinfolta

Cameron J. English is the GLPs managing editor. BIO. Follow him on Twitter @camjenglish

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Podcast: Doctors have to think about sex; AI text generators spread 'fake news'? Coffee can indeed make you poop - Genetic Literacy Project