Monthly Archives: January 2022

Meta founder Mark Zuckerberg was met with a mixed reaction after touting his company's "exciting breakthrough" towards creating an artificial intelligence system that thinks "the way we do."

In a Facebook post on Thursday, Zuckerberg hailed the development of Meta's data2vec, a new artificial intelligence algorithm that is capable of learning about several different types of information without supervision. Zuckerberg predicted that the development could eventually be used to more effectively help people perform common tasks like cooking.

"Exciting breakthrough: Meta AI research built a system that learns from speech, vision and text without needing labeled training data," Zuckerberg wrote in the post. "People experience the world through a combination of sight, sound and words, and systems like this could one day understand the world the way we do."

"This will all eventually get built into AR glasses with an AI assistant so, for example, it could help you cook dinner, noticing if you miss an ingredient, prompting you to turn down the heat, or more complex tasks," he added.

See posts, photos and more on Facebook.

Although previous artificial intelligence systems have also used self-supervised learning, they have not been able to learn more than one type of information effectivelyfor example, a system that is able to decipher text effectively may be unable to interpret information from images.

A blog post from Meta developers described data2vec as "the first high-performance self-supervised algorithm that works for multiple modalities." The developers noted that the data2vec performed better than multiple single-use algorithms and said that it "brings us closer to building machines that learn seamlessly about different aspects of the world around them."

"The core idea of this approach is to enable AI to learn more generally: AI should be able to learn to do many different tasks, including ones that are entirely unfamiliar," a Meta spokesperson said in a statement to Newsweek. "We want a machine to not only recognize animals shown in its training data but also adapt to recognize new creatures in an environment if we tell it what they look like."

"The hope is that algorithms like this one will lead to powerful multi-modal, self-learning AI models," the spokesperson continued. "Meaning AI that can make sense of the physical and virtual worlds around us using all the senses that humans do simultaneously."

While many responded to Zuckerberg's post by congratulating him and sharing in his excitement about the potential applications of data2vec, others responded to his post by expressing fears that the "creepy" development could lead to a "nightmarish dystopia."

"The potential benefits of this are far outweighed by the unimaginable nightmarish dystopia it will create," Facebook user Brendon Shapiro wrote in response to Zuckerberg's post. "If I forget the lemon zest, well, it'll just have to be ok."

"I've said this loads of times before.. I'm getting Skynet vibes.. we've all seen The Terminator," wrote actor Ritchi Edwards, referring to the film franchise's fictional artificial intelligence network that becomes sentient and begins to attack humanity.

"Ummmm... this kinda sounds creepy," Facebook user Rachel Miller wrote. "I am literally one of Facebooks most vocal fans... but this... an intuitive Alexa?? I don't know if I want it telling me to pick up the socks on the floor or telling me to add more cinnamon..."

Meta's new development does bring artificial intelligence closer to the goal of replicating human-like learning and thinking. However, the algorithm is still far removed from the creation of an autonomous system that could represent any kind of realistic threat to people if left unchecked.

Update 01/21/22, 6:50 p.m. ET:This article has been updated to include a statement from a Meta spokesperson.

Read more:

Frightening Reality of Meta-Built Artificial Intelligence That Can Think 'the Way We Do' - Newsweek

While mammograms are currently the gold standard in breast cancer screening, swirls of controversy exist regarding when and how often they should be administered. On the one hand, advocates argue for the ability to save lives: Women aged 60-69 who receive mammograms, for example, have a 33 percent lower risk of dying compared to those who dont get mammograms. Meanwhile, others argue about costly and potentially traumatic false positives: A meta-analysis of three randomized trials found a 19 percent over-diagnosis rate from mammography.

Even with some saved lives, and some overtreatment and overscreening, current guidelines are still a catch-all: Women aged 45 to 54 should get mammograms every year. While personalized screening has long been thought of as the answer, tools that can leverage the troves of data to do this lag behind.

This led scientists from MITs Computer Science and Artificial Intelligence Laboratory (CSAIL) and Jameel Clinic for Machine Learning and Health to ask: Can we use machine learning to provide personalized screening?

Out of this came Tempo, a technology for creating risk-based screening guidelines. Using an AI-based risk model that looks at who was screened and when they got diagnosed, Tempo will recommend a patient return for a mammogram at a specific time point in the future, like six months or three years. The same Tempo policy can be easily adapted to a wide range of possible screening preferences, which would let clinicians pick their desired early-detection-to-screening-cost trade-off, without training new policies.

The model was trained on a large screening mammography dataset from Massachusetts General Hospital (MGH), and was tested on held-out patients from MGH as well as external datasets from Emory, Karolinska Sweden, and Chang Gung Memorial hospitals. Using the teams previously developed risk-assessment algorithm Mirai, Tempo obtained better early detection than annual screening while requiring 25 percent fewer mammograms overall at Karolinska. At MGH, it recommended roughly a mammogram a year, and obtained a simulated early detection benefit of roughly four-and-a-half months better.

By tailoring the screening to the patient's individual risk, we can improve patient outcomes, reduce overtreatment, and eliminate health disparities, says Adam Yala, a PhD student in electrical engineering and computer science, MIT CSAIL affiliate, and lead researcher on a paper describing Tempo published Jan. 13 in Nature Medicine. Given the massive scale of breast cancer screening, with tens of millions of women getting mammograms every year, improvements to our guidelines are immensely important.

Early uses of AI in medicine stem back to the 1960s, where many refer to the Dendral experiments as kicking off the field. Researchers created a software system that was considered the first expert kind that automated the decision-making and problem-solving behavior of organic chemists. Sixty years later, deep medicine has greatly evolved drug diagnostics, predictive medicine, and patient care.

Current guidelines divide the population into a few large groups, like younger or older than 55, and recommend the same screening frequency to all the members of a cohort. The development of AI-based risk models that operate over raw patient data give us an opportunity to transform screening, giving more frequent screens to those who need it and sparing the rest, says Yala. A key aspect of these models is that their predictions can evolve over time as a patients raw data changes, suggesting that screening policies need to be attuned to changes in risk and be optimized over long periods of patient data.

Tempo uses reinforcement learning, a machine learning method widely known for success in games like Chess and Go, to develop a policy that predicts a followup recommendation for each patient.

The training data here only had information about a patients risk at the time points when their mammogram was taken (when they were 50, or 55, for example). The team needed the risk assessment at intermediate points, so they designed their algorithm to learn a patients risk at unobserved time points from their observed screenings, which evolved as new mammograms of the patient became available.

The team first trained a neural network to predict future risk assessments given previous ones. This model then estimates patient risk at unobserved time points, and it enables simulation of the risk-based screening policies. Next, they trained that policy, (also a neural network), to maximize the reward (for example, the combination of early detection and screening cost) to the retrospective training set. Eventually, youd get a recommendation for when to return for the next screen, ranging from six months to three years in the future, in multiples of six months the standard is only one or two years.

Lets say Patient A comes in for their first mammogram, and eventually gets diagnosed at Year Four. In Year Two, theres nothing, so they dont come back for another two years, but then at Year Four they get a diagnosis. Now there's been two years of gap between the last screen, where a tumor could have grown.

Using Tempo, at that first mammogram, Year Zero, the recommendation might have been to come back in two years. And then at Year Two, it might have seen that risk is high, and recommended that the patient come back in six months, and in the best case, it would be detectable. The model is dynamically changing the patients screening frequency, based on how the risk profile is changing.

Tempo uses a simple metric for early detection, which assumes that cancer can be caught up to 18 months in advance. While Tempo outperformed current guidelines across different settings of this assumption (six months, 12 months), none of these assumptions are perfect, as the early detection potential of a tumor depends on that tumor's characteristics. The team suggested that follow-up work using tumor growth models could address this issue.

Also, the screening-cost metric, which counts the total screening volume recommended by Tempo, doesn't provide a full analysis of the entire future cost because it does not explicitly quantify false positive risks or additional screening harms.

There are many future directions that can further improve personalized screening algorithms. The team says one avenue would be to build on the metrics used to estimate early detection and screening costs from retrospective data, which would result in more refined guidelines. Tempo could also be adapted to include different types of screening recommendations, such as leveraging MRI or mammograms, and future work could separately model the costs and benefits of each. With better screening policies, recalculating the earliest and latest age that screening is still cost-effective for a patient might be feasible.

Our framework is flexible and can be readily utilized for other diseases, other forms of risk models, and other definitions of early detection benefit or screening cost. We expect the utility of Tempo to continue to improve as risk models and outcome metrics are further refined. We're excited to work with hospital partners to prospectively study this technology and help us further improve personalized cancer screening, says Yala.

Yala wrote the paper on Tempo alongside MIT PhD student Peter G. Mikhael, Fredrik Strand of Karolinska University Hospital, Gigin Lin of Chang Gung Memorial Hospital, Yung-Liang Wan of Chang Gung University, Siddharth Satuluru of Emory University, Thomas Kim of Georgia Tech, Hari Trivedi of Emory University, Imon Banerjee of the Mayo Clinic, Judy Gichoya of the Emory University School of Medicine, Kevin Hughes of MGH, Constance Lehman of MGH, and senior author and MIT Professor Regina Barzilay.

The research is supported by grants from Susan G. Komen, Breast Cancer Research Foundation, Quanta Computing, an Anonymous Foundation, the MIT Jameel-Clinic, Chang Gung Medical Foundation Grant, and by Stockholm Lns Landsting HMT Grant.

Follow this link:

Seeing into the future: Personalized cancer screening with artificial intelligence - MIT News

Reporter Chloe Veltman reacts to hearing her digital voice double, "Chloney," for the first time, with Speech Morphing chief linguist Mark Seligman. Courtesy of Speech Morphing hide caption

Reporter Chloe Veltman reacts to hearing her digital voice double, "Chloney," for the first time, with Speech Morphing chief linguist Mark Seligman.

The science behind making machines talk just like humans is very complex, because our speech patterns are so nuanced.

"The voice is not easy to grasp," says Klaus Scherer, emeritus professor of the psychology of emotion at the University of Geneva. "To analyze the voice really requires quite a lot of knowledge about acoustics, vocal mechanisms and physiological aspects. So it is necessarily interdisciplinary, and quite demanding in terms of what you need to master in order to do anything of consequence."

So it's not surprisingly taken well over 200 years for synthetic voices to get from the first speaking machine, invented by Wolfgang von Kempelen around 1800 a boxlike contraption that used bellows, pipes and a rubber mouth and nose to simulate a few recognizably human utterances, like mama and papa to a Samuel L. Jackson voice clone delivering the weather report on Alexa today.

A model replica of Wolfgang von Kempelen's Speaking Machine. Fabian Brackhane hide caption

A model replica of Wolfgang von Kempelen's Speaking Machine.

Talking machines like Siri, Google Assistant and Alexa, or a bank's automated customer service line, are now sounding quite human. Thanks to advances in artificial intelligence, or AI, we've reached a point where it's sometimes difficult to distinguish synthetic voices from real ones.

I wanted to find out what's involved in the process at the customer end. So I approached San Francisco Bay Area-based natural language speech synthesis company Speech Morphing about creating a clone or "digital double" of my own voice.

Given the complexities of speech synthesis, it's quite a shock to find out just how easy it is to order one up. For a basic conversational build, all a customer has to do is record themselves saying a bunch of scripted lines for roughly an hour. And that's about it.

"We extract 10 to 15 minutes of net recordings for a basic build," says Speech Morphing founder and CEO Fathy Yassa.

The hundreds of phrases I record so that Speech Morphing can build my digital voice double seem very random: "Here the explosion of mirth drowned him out." "That's what Carnegie did." "I'd like to be buried under Yankee Stadium with JFK." And so on.

But they aren't as random as they appear. Yassa says the company chooses utterances that will produce a wide enough variety of sounds across a range of emotions such as apologetic, enthusiastic, angry and so on to feed a neural network-based AI training system. It essentially teaches itself the specific patterns of a person's speech.

Speech Morphing founder and CEO Fathy Yassa. Chloe Veltman/KQED hide caption

Speech Morphing founder and CEO Fathy Yassa.

Yassa says there are around 20 affects or tones to choose from, and some of these can be used interchangeably, or not at all. "Not every tone or affect is needed for every client," he says. "The choice depends on the target application and use cases. Banking is different from eBooks, is different from reporting and broadcast, is different from consumer."

At the end of the recording session, I send Speech Morphing the audio files. From there, the company breaks down and analyzes my utterances, and then builds the model for the AI to learn from. Yassa says the entire process takes less than a week.

He says the possibilities for the Chloe Veltman voice clone or "Chloney" as I've affectionately come to call my robot self are almost limitless.

"We can make you apologetic, we can make you promotional, we can make you act like you're in the theater," Yassa says. "We can make you sing, eventually, though we're not yet there."

The global speech and voice recognition industry is worth tens of billions of dollars,and is growing fast. Its uses are evident. The technology has given actor Val Kilmer, who lost his voice owing to throat cancer a few years ago, the chance to reclaim something approaching his former vocal powers.

It's enabled film directors, audiobook creators and game designers to develop characters without the need to have live voice talent on hand, as in the movie Roadrunner, where an AI was trained on Anthony Bourdain's extensive archive of media appearances to create a digital double of the late chef and TV personality's voice.

As pitch-perfect as Bourdain's digital voice double might be, it's also caused controversy. Some people raised ethical concerns about putting words into Bourdain's mouth that he never actually said while he was alive.

A cloned version of Barack Obama's voice warning people about the dangers of fake news, created by actor and film director Jordan Peele, hammers the point home: Sometimes we have cause to be wary of machines that sound too much like us.

[Note: The video embedded below includes profanities.]

"We're entering an era in which our enemies can make it look like anyone is saying anything at any point in time," says the Obama deepfake in the video, produced in collaboration with BuzzFeed in 2018. "Even if they would never say those things."

Sometimes, though, we don't necessarily want machines to sound too human, because it creeps us out.

If you're looking for a digital voice double to read an audiobook to kids, or act as a companion or helper for a senior, a more human-sounding voice might be the right way to go.

"Maybe not something that actually breathes, because that's a little bit creepy, but a little more human might be more approachable," says user experience and voice designer Amy Jimnez Mrquez, who led the voice, multimodal and UX Amazon Alexa personality-experience design team for four years.

But for a machine that performs basic tasks, like, say, a voice-activated refrigerator? Maybe less human is best. "Having something a little more robotic and you can even create a tinny voice that sounds like an actual robot that is cute, that would be more appropriate for a refrigerator," Jimnez Mrquez says.

At a demo session with Speech Morphing, I get to hear Chloney, my digital voice double.

Her voice comes at me through a pair of portable speakers connected to a laptop. The laptop displays the programming interface into which whatever text I want Chloney to say is typed. The interface includes tools to make micro-adjustments to the pitch, speed and other vocal attributes that might need to be tweaked if Chloney's prosody doesn't come out sounding exactly right.

"Happy birthday to you. Happy birthday to you. Happy birthday, dear Chloney. Happy birthday to you," says Chloney.

Chloney can't sing "Happy Birthday" at least for now. But she can read out news stories I didn't even report myself, like one ripped from an AP newswire about the COVID-19 pandemic. And she can even do it in Spanish.

Chloney sounds quite a lot like me. It's impressive, but it's also a little scary.

"My jaw is on the floor," says the original voice behind Chloney that's me, Chloe as I listen to what my digital voice double can do. "Let's hope she doesn't put me out of a job anytime soon."

Follow this link:

Artificial intelligence is being used to digitally replicate human voices - NPR

French VFX powerhouse MacGuff with headquarters in Paris and offices in L.A. is using proprietary artificial intelligence tools, in particular Face Engine and Body Engine, in a broad range of VFX projects.

Current projects in the pipeline include Season 2 of Lupin for Netflix, Htel du temps for France Tlvisions, and Christian Carions Une belle course, starring Dany Boon. The studio also used AI tools in ric Rochants political thriller series The Bureau.

Htel du temps is a perfect example of the power of Face Engine since it brings historic figures back to life, such as late actor Jean Gabin and Princess Diana, to be interviewed by hard-hitting French journalist Thierry Ardisson.

MacGuff has an in-house R&D department that has been developing proprietary AI tools by mixing open-source software with proprietary code. The AI developments are being overseen by co-founder and joint director Rodolphe Chabrier and MacGuffs veteran VFX supervisor Martial Vallanchon.

MacGuff recently received a 200,000 euros ($230,000) grant from Frances CNC to expand its AI engine, as part of the CNCs $11.4 million technological modernization scheme launched in 2021, which has provided support for 20 French studios and digital post-production companies.

Our AI tools can make people look younger and older, or even bring people back to life! explains Philippe Sonrier, MacGuffs other co-founder and joint director. We were the first studio to develop these tools in Europe. They deliver new narrative options and the chance to make more complex characters.

Sonrier adds: AI is totally different from the method that we have known for the past 30 years, primarily based on complex and time-consuming synthesis methods [modeling, rigging, motion capture, photoreal rendering]. AI brings elements of reality in effects. Its amazing how it makes the images more natural. For example, you can film the movements of an actor and a dancer and then merge the two. Its going to revolutionize our industry.

MacGuff was founded in Paris in 1986. In mid-2011 it split into two companies. Universal bought the animation department, renamed as Illumination MacGuff, run by Jacques Bled.

Sonrier is also co-president of FranceVFX, the French visual effects vendors association, created in 2017, which represents 12 studios: MacGuff, BUF, Digital District, Mikros Image, Trimaran, Solidanim, The Yard, Autre Chose, Les Androds Associs, Reepost, La Plante Rouge and D-Seed.

FranceVFX is a lobby for VFX interests and also serves as a liaison mechanism between the participating members. It has facilitated joint cooperation on more ambitious VFX projects.

One recent example was Martin Bourboulons historical drama Eiffel, with 560 VFX shots made by Buf, MacGuff and CGEV, for which the overall VFX supervisor was Olivier Cauwet. Major VFX work is also being developed for Bourboulons upcoming The Three Musketeers DArtagnan and The Three Musketeers Milady, a $85 million two-part saga based on Alexandre Dumas masterpiece.

Collaboration on VFX projects between various studios is a new model for France that has been tested successfully in the U.S., Canada and the U.K., explains Sonrier. It permits us to be more secure. If one vendor has a problem with a project, we can help each other out. The position of the VFX supervisor is emerging in France.

MacGuff works on major international projects as well as French films and series. It produced VFX work on Julia Ducournaus 2021 Cannes Palme dOr winner, Titane, including the CGI sequences that created the car baby.

Our culture is inevitably very French, explains Sonrier. We like to be very close to the creative decisions and become involved in each project as soon as possible. Thats part of our DNA. Titane is a good example. Initially the director tried out animatronics solutions but wasnt happy with them. We used CGI to create the car baby. Its something wed previously tried out in 2006 when creating a fetus for the French documentary Lodysse de la Vie by Nils Tavernier.

VFX work is always very risky in both creative and financial terms, says Sonrier. This is particularly true in the French tradition, because of the status of the director as the auteur and supreme decision-maker.

For major international films and series, Sonrier considers that it is easier to lock down the logistics, but sometimes at the cost of becoming more like a factory pipeline. MacGuff has forged a strong relationship with Netflix, which was cemented by its VFX work on its gentleman thief series Lupin. Another major VFX job produced for Netflix was Alexandre Ajas 2021 survival thriller Oxygen, where the VFX work alone was budgeted at over 1 million ($1.14 million).

MacGuff is now working on a major international series, which involves coordination between several VFX studios. It is also working on a major animation project between France, Belgium and Canada, and an ambitious French robot-themed project that will begin lensing in mid-2022.

More international projects are coming to France in the wake of the change introduced in 2020 to Frances Tax Rebate for International Production (TRIP) scheme, which now offers a 40% rebate on all eligible expenses including for live action spends that are not VFX related for international projects whose VFX expenses surpass 2 million ($2.27 million) spent in France.

High-profile projects attracted by this change include Ridley Scotts 14th century period epic The Last Duel, with VFX work done by Mikros Image. Another example is the 16th-century Medici drama Serpent Queen, produced for Starz by Lionsgate Television and 3 Arts Entertainment.

Smaller-scale international projects can apply for other support mechanisms such as the CVS scheme for ambitious visual and sound projects. The CVS scheme was used on the Lithuanian-French production Vesper Seeds, for which the VFX work was shared with Mathematic, Mikros Liege and Excuse My French.

This dystopian pic, set after the collapse of the Earths ecosystem, is the third feature from Lithuanian helmer Kristina Buozyte and French helmer Bruno Samper, who co-directed a short segment for the 2014 American horror anthology ABCs of Death 2.

MacGuff is also producing VFX work for the documentary Corridor of Power, produced by Dror Moreh, having previously worked on other projects produced by him, such as Oscar-nominee The Gatekeepers and The Human Factor, which won the Grand Prix at Fipadoc 2020.

Other recent French productions handled by the studio include Nicolas Girauds Lastronaute, starring Giraud and Mathieu Kassovitz.

MacGuff is a long-time collaborator of French-Argentine helmer Gaspar No and did the VFX work on his latest feature film, Vortex, in terms of stabilization of the frames, rotations, small morphs, retiming and adjustments to the split screen images.

The studio also provides VFX for documentaries, such as La rafle des notables, produced by Victor Roberts 10.7 Productions, based on Anne Sinclairs book about French concentration camps during World War II. It is also working on a docufiction from 10.7 Productions The Last Secrets of Humanity, directed by Jacques Malaterre, about the prehistoric period in China, including VFX work to recreate prehistoric animals, jointly produced by Mikros and MacGuff.

The company is developing VR/AR and immersive projects, primarily commercials, via its subsidiary Small.

See the rest here:

MacGuffs Philippe Sonrier on How Artificial Intelligence Tools Will Revolutionize the VFX Industry - Variety

MANAMA, Bahrain U.S. Navy leaders are emphasizing unmanned surface vessels (USVs) in their testbed effort for new platforms operating in U.S. Central Command, says Vice Adm. Brad Cooper, commander of the U.S. 5th Fleet in Manama, Bahrain. USNI News reports. Continue reading original article

The Military & Aerospace Electronics take:

19 Jan. 2022 --Starting this month, the International Maritime Exercise 22 will build on a special unmanned group that has been operating in the 5th Fleet since September. Ten of 60 nations are bringing autonomous surface vessels to the exercise in what will be the largest unmanned exercise in the world. Task Force 59, created last year to help the Navy expand its unmanned systems testing, quickly evolved into working with regional partners -- first with Bahrain, and then Jordan.

Ten of those nations are bringing unmanned platforms. Itll be the largest unmanned exercise in the world, Cooper, the commander of 5th Fleet, said at an event co-hosted by the Center for Strategic and International Studies and the U.S. Naval Institute.

Task Force 59, created last year to help the Navy expand its unmanned systems testing across domains, quickly evolved into working with regional partners first with Bahrain, and then Jordan. Were taking off-the-shelf emerging technology in unmanned, coupling with artificial intelligence (AI) and machine learning, in really moving at pace to bring new capabilities to the region," Cooper says.

Related: Unmanned submarines seen as key to dominating the worlds oceans

Related: Artificial intelligence and machine learning for unmanned vehicles

Related: Artificial intelligence and embedded computing for unmanned vehicles

John Keller, chief editorMilitary & Aerospace Electronics

Excerpt from:

Navy emphasizing unmanned surface vessels (USVs) and artificial intelligence (AI) in Middle East operations - Military & Aerospace Electronics

Artificial intelligence and drones are useful for transforming Indian policing in 2022

AI can be a powerful tool for law enforcement and help in addressing many types of crimes. It can help law enforcement to optimize their resources in specific areas and at specific times, to cover as much ground as possible with the same or even fewer resources. Drones with sensors, for instance, can also be used to detect illegal movements such as illegal border crossings, human traffickers, and vessels illegally fishing. Location is a powerful piece of information for AI systems. In India too, artificial intelligence tools are increasingly being put to use. The police departments use of technology is not just limited to facial recognition. It has also been using tools for predictive policing such as crime mapping, analytics, and predictive system, a predictive system that analyses data from past and current phone calls to police hotlines to predict the time and nature of criminal activities in hotspots across the city.

When the respondents were asked about the lack of an Indian police force, the majority of them i.e. 45.8% of 251 respondents, voted for relationship between police and the public as the key factor lacking in the Indian police force. Out of the total, 27.1% respondents selected police accountability. 13.5% of the total respondents selected overburdened force and vacancies, while 11.5% think the process involved in investigation of crime is a lacking factor for the Indian police. The remaining 2.1% think its the infrastructure where Indian police lack.

public

There are numerous solutions for the Indian police force that can make it better. From the survey, it is observed that 63% of 251 respondents think that limiting the political executives power of superintendence over police forces will be the best solution for the Indian police force. 15.2% responded to the specialized investigating units as the key solution to the Indian police force. 11.6% of the respondents selected the Community policing model while the rest 10.2% selected Outsourcing and redistributing functions as the best solution for the Indian police force.

From the survey, it is found that 56% of the total respondents think that AI cops must be given a chance over the regular police force, 29.2% think that AI cops may be given a chance, while the remaining 14.6% think that AI cops should not be given a chance over the regular police force.

Share This ArticleDo the sharing thingy

About AuthorMore info about author

Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

See the original post here:

The Future of Indian Policing with Artificial Intelligence in 2022 and Beyond - Analytics Insight

Jan 20, 2022 11:50 AM

System mines Electronic Health Records (EHRs) to assess combined effects of various risk factors

For the first time, University of Utah Health scientists have shown that artificial intelligence could lead to better ways to predict the onset and course of cardiovascular disease. The researchers, working in conjunction with physicians from Intermountain Primary Childrens Hospital, developed unique computational tools to precisely measure the synergistic effects of existing medical conditions on the heart and blood vessels.

The researchers say this comprehensive approach could help physicians foresee, prevent, or treat serious heart problems, perhaps even before a patient is aware of the underlying condition.

We can turn to AI to help refine the risk for virtually every medical diagnosis

Although the study only focused on cardiovascular disease, the researchers believe it could have far broader implications. In fact, they suggest that these findings could eventually lead to a new era of personalized, preventive medicine. Doctors would proactively contact patients to alert them to potential ailments and what can be done to alleviate the problem.

We can turn to AI to help refine the risk for virtually every medical diagnosis, says Martin Tristani-Firouzi, M.D. the studys corresponding author and a pediatric cardiologist at U of U Health and Intermountain Primary Childrens Hospital, and scientist at the Nora Eccles Harrison Cardiovascular Research and Training Institute. The risk of cancer, the risk of thyroid surgery, the risk of diabetesany medical term you can imagine.

The study appears in the online journal PLOS Digital Health.

Current methods for calculating the combined effects of various risk factorssuch as demographics and medical historyon cardiovascular disease are often imprecise and subjective, according to Mark Yandell, Ph.D., senior author of the study, a professor of human genetics, H.A. and Edna Benning Presidential Endowed Chair at U of U Health, and co-founder of Backdrop Health. As a result, these methods fail to identify certain interactions that could have profound effects on the health of the heart and blood vessels.

To more accurately measure how these interactions, also known as comorbidities, influence health, Tristani-Firouzi, Yandell, and colleagues from U of U Health and Intermountain Primary Childrens Hospital, used machine learning software to sort through more than 1.6 million electronic health records (EHRs) after names and other identifying information were deleted.

These electronic records, which document everything that happens to a patient, including lab tests, diagnoses, medication usage, and medical procedures, helped the researchers identify the comorbidities most likely to aggravate a particular medical condition such as cardiovascular disease.

In their current study, the researchers used a form of artificial intelligence called probabilistic graphical networks (PGM) to calculate how any combination of these comorbidities could influence the risks associated with heart transplants, congenital heart disease, or sinoatrial node dysfunction (SND, a disruption or failure of the hearts natural pacemaker).

Among adults, the researchers found that:

In some instances, the combined risk was even greater. For instance, among patients who had cardiomyopathy and were taking milrinone, the risk of needing a heart transplant was 405 times higher than it was for those whose hearts were healthier.

Comorbidities had a significantly different influence on the transplant risk among children, according to Tristani-Firouzi. Overall, the risk of pediatric heart transplant ranged from 17 to 102 times higher than children who didnt have pre-existing heart conditions, depending on the underlying diagnosis.

The researchers also examined influences that a mothers health during pregnancy had on her children. Women who had high blood pressure during pregnancy were about twice as likely to give birth to infants who had congenital heart and circulatory problems. Children with Down syndrome had about three times greater risk of having a heart anomaly.

Infants who had Fontan surgery, a procedure that corrects a congenital blood flow defect in the heart, were about 20 times more likely to develop SND heart rate dysfunction than those who didnt need the surgery

The researchers also detected important demographic differences. For instance, a Hispanic patient with atrial fibrillation (rapid heartbeat) had twice the risk of SND compared with Blacks and Whites, who had similar medical histories.

Josh Bonkowsky, M.D. Ph.D., Director of the Primary Childrens Center for Personalized Medicine, who is not an author on the study, believes this research could lead to development of a practical clinical tool for patient care.

This novel technology demonstrates that we can estimate the risk for medical complications with precision and can even determine medicines that are better for individual patients. Bonkowsky says.

Moving forward, Tristani-Firouzi and Yandell hope their research will also help physicians untangle the growing web of disorienting medical information enveloping them every day.

No matter how aware you are, theres no way to keep all of the knowledge that you need in your head as a medical professional in this day and age to treat patients in the best way possible, Yandell says. The computational machines we are developing will help physicians make the best possible patient care decisions, using all of the pertinent information available in our electronic age. These machines are vital to the future of medicine.

####

This research was published online on January 18, 2022 as, An Explainable Artificial Intelligence Approach for Predicting Cardiovascular Outcomes using Electronic Health Records.

In addition to Drs. Tristani-Firouzi and Yandell, University of Utah Health scientists contributing to this research were S. Wesolowski, G. Lemmon, E.J. Hernandez, A. Henrie, T.A. Miller, D. Wyhrauch, M.D. Puchalski, B.E. Bray, R.U. Shah, V.G. Deshmukh, R. Delaney, H.J. Yost, and K. Eilbeck.

The study was supported by the AHA Childrens Strategically Focused Research Network, the Nora Eccles Treadwell Foundation, and the National Heart, Lung and Blood Institute.

Competing interests: Yandell, Deshmukh and Lemmon own shares in Backdrop Health; there are no financial ties regarding this research.

Research News genomics cardiology

Read the original here:

Artificial Intelligence Identifies Individuals at Risk for Heart Disease Complications - University of Utah Health Care

In the fall of 2019, Google told the world it had reached quantum supremacy.

It was a significant scientific milestone that some compared to the first flight at Kitty Hawk. Harnessing the mysterious powers of quantum mechanics, Google had built a computer that needed only three minutes and 20 seconds to perform a calculation that normal computers couldnt complete in 10,000 years.

But more than two years after Googles announcement, the world is still waiting for a quantum computer that actually does something useful. And it will most likely wait much longer. The world is also waiting for self-driving cars, flying cars, advanced artificial intelligence and brain implants that will let you control your computing devices using nothing but your thoughts.

Silicon Valleys hype machine has long been accused of churning ahead of reality. But in recent years, the tech industrys critics have noticed that its biggest promises the ideas that really could change the world seem further and further on the horizon. The great wealth generated by the industry in recent years has generally been thanks to ideas, like the iPhone and mobile apps, that arrived years ago.

Have the big thinkers of tech lost their mojo?

The answer, those big thinkers are quick to respond, is absolutely not. But the projects they are tackling are far more difficult than building a new app or disrupting another aging industry. And if you look around, the tools that have helped you cope with almost two years of a pandemic the home computers, the videoconferencing services and Wi-Fi, even the technology that aided researchers in the development of vaccines have shown the industry hasnt exactly lost a step.

Imagine the economic impact of the pandemic had there not been the infrastructure the hardware and the software that allowed so many white-collar workers to work from home and so many other parts of the economy to be conducted in a digitally mediated way, said Margaret OMara, a professor at the University of Washington who specializes in the history of Silicon Valley.

As for the next big thing, the big thinkers say, give it time. Take quantum computing. Jake Taylor, who oversaw quantum computing efforts for the White House and is now chief science officer at the quantum start-up Riverlane, said building a quantum computer might be the most difficult task ever undertaken. This is a machine that defies the physics of everyday life.

A quantum computer relies on the strange ways that some objects behave at the subatomic level or when exposed to extreme cold, like metal chilled to nearly 460 degrees below zero. If scientists merely try to read information from these quantum systems, they tend to break.

While building a quantum computer, Dr. Taylor said, you are constantly working against the fundamental tendency of nature.

The most important tech advances of the past few decades the microchip, the internet, the mouse-driven computer, the smartphone were not defying physics. And they were allowed to gestate for years, even decades, inside government agencies and corporate research labs before ultimately reaching mass adoption.

The age of mobile and cloud computing has created so many new business opportunities, Dr. OMara said. But now there are trickier problems.

Still, the loudest voices in Silicon Valley often discuss those trickier problems as if they were just another smartphone app. That can inflate expectations.

People who arent experts who understand the challenges may have been misled by the hype, said Raquel Urtasun, a University of Toronto professor who helped oversee the development of self-driving cars at Uber and is now chief executive of the self-driving start-up Waabi.

Technologies like self-driving cars and artificial intelligence do not face the same physical obstacles as quantum computing. But just as researchers do not yet know how to build a viable quantum computer, they do not yet know how to design a car that can safely drive itself in any situation or a machine that can do anything the human brain can do.

Even a technology like augmented reality eyeglasses that can layer digital images onto what you see in the real world will require years of additional research and engineering before it is perfected.

Andrew Bosworth, vice president at Meta, formerly Facebook, said that building these lightweight eyeglasses was akin to creating the first mouse-driven personal computers in the 1970s (the mouse itself was invented in 1964). Companies like Meta must design an entirely new way of using computers, before stuffing all its pieces into a tiny package.

Over the past two decades, companies like Facebook have built and deployed new technologies at a speed that never seemed possible before. But as Mr. Bosworth said, these were predominantly software technologies built solely with bits pieces of digital information.

Building new kinds of hardware working with physical atoms is a far more difficult task. As an industry, we have almost forgotten what this is like, Mr. Bosworth said, calling the creation of augmented reality glasses a once-in-a-lifetime project.

Technologists like Mr. Bosworth believe they will eventually overcome those obstacles and they are more open about how difficult it will be. But thats not always the case. And when an industry has seeped into every part of daily life, it can be hard to separate hand-waving from realism especially when it is huge companies like Google and well-known personalities like Elon Musk drawing that attention.

Many in Silicon Valley believe that hand-waving is an important part of pushing technologies into the mainstream. The hype helps attract the money and the talent and the belief needed to build the technology.

If the outcome is desirable and it is technically possible then its OK if were off by three years or five years or whatever, said Aaron Levie, chief executive of the Silicon Valley company Box. You want entrepreneurs to be optimistic to have a little bit of that Steve Jobs reality-distortion field, which helped to persuade people to buy into his big ideas.

The hype is also a way for entrepreneurs to generate interest among the public. Even if new technologies can be built, there is no guarantee that people and businesses will want them and adopt them and pay for them. They need coaxing. And maybe more patience than most people inside and outside the tech industry will admit.

When we hear about a new technology, it takes less than 10 minutes for our brains to imagine what it can do. We instantly compress all of the compounding infrastructure and innovation needed to get to that point, Mr. Levie said. That is the cognitive dissonance we are dealing with.

Go here to see the original:

Why Is Silicon Valley Still Waiting for the Next Big Thing? - The New York Times

Country

United States of AmericaUS Virgin IslandsUnited States Minor Outlying IslandsCanadaMexico, United Mexican StatesBahamas, Commonwealth of theCuba, Republic ofDominican RepublicHaiti, Republic ofJamaicaAfghanistanAlbania, People's Socialist Republic ofAlgeria, People's Democratic Republic ofAmerican SamoaAndorra, Principality ofAngola, Republic ofAnguillaAntarctica (the territory South of 60 deg S)Antigua and BarbudaArgentina, Argentine RepublicArmeniaArubaAustralia, Commonwealth ofAustria, Republic ofAzerbaijan, Republic ofBahrain, Kingdom ofBangladesh, People's Republic ofBarbadosBelarusBelgium, Kingdom ofBelizeBenin, People's Republic ofBermudaBhutan, Kingdom ofBolivia, Republic ofBosnia and HerzegovinaBotswana, Republic ofBouvet Island (Bouvetoya)Brazil, Federative Republic ofBritish Indian Ocean Territory (Chagos Archipelago)British Virgin IslandsBrunei DarussalamBulgaria, People's Republic ofBurkina FasoBurundi, Republic ofCambodia, Kingdom ofCameroon, United Republic ofCape Verde, Republic ofCayman IslandsCentral African RepublicChad, Republic ofChile, Republic ofChina, People's Republic ofChristmas IslandCocos (Keeling) IslandsColombia, Republic ofComoros, Union of theCongo, Democratic Republic ofCongo, People's Republic ofCook IslandsCosta Rica, Republic ofCote D'Ivoire, Ivory Coast, Republic of theCyprus, Republic ofCzech RepublicDenmark, Kingdom ofDjibouti, Republic ofDominica, Commonwealth ofEcuador, Republic ofEgypt, Arab Republic ofEl Salvador, Republic ofEquatorial Guinea, Republic ofEritreaEstoniaEthiopiaFaeroe IslandsFalkland Islands (Malvinas)Fiji, Republic of the Fiji IslandsFinland, Republic ofFrance, French RepublicFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabon, Gabonese RepublicGambia, Republic of theGeorgiaGermanyGhana, Republic ofGibraltarGreece, Hellenic RepublicGreenlandGrenadaGuadaloupeGuamGuatemala, Republic ofGuinea, RevolutionaryPeople's Rep'c ofGuinea-Bissau, Republic ofGuyana, Republic ofHeard and McDonald IslandsHoly See (Vatican City State)Honduras, Republic ofHong Kong, Special Administrative Region of ChinaHrvatska (Croatia)Hungary, Hungarian People's RepublicIceland, Republic ofIndia, Republic ofIndonesia, Republic ofIran, Islamic Republic ofIraq, Republic ofIrelandIsrael, State ofItaly, Italian RepublicJapanJordan, Hashemite Kingdom ofKazakhstan, Republic ofKenya, Republic ofKiribati, Republic ofKorea, Democratic People's Republic ofKorea, Republic ofKuwait, State ofKyrgyz RepublicLao People's Democratic RepublicLatviaLebanon, Lebanese RepublicLesotho, Kingdom ofLiberia, Republic ofLibyan Arab JamahiriyaLiechtenstein, Principality ofLithuaniaLuxembourg, Grand Duchy ofMacao, Special Administrative Region of ChinaMacedonia, the former Yugoslav Republic ofMadagascar, Republic ofMalawi, Republic ofMalaysiaMaldives, Republic ofMali, Republic ofMalta, Republic ofMarshall IslandsMartiniqueMauritania, Islamic Republic ofMauritiusMayotteMicronesia, Federated States ofMoldova, Republic ofMonaco, Principality ofMongolia, Mongolian People's RepublicMontserratMorocco, Kingdom ofMozambique, People's Republic ofMyanmarNamibiaNauru, Republic ofNepal, Kingdom ofNetherlands AntillesNetherlands, Kingdom of theNew CaledoniaNew ZealandNicaragua, Republic ofNiger, Republic of theNigeria, Federal Republic ofNiue, Republic ofNorfolk IslandNorthern Mariana IslandsNorway, Kingdom ofOman, Sultanate ofPakistan, Islamic Republic ofPalauPalestinian Territory, OccupiedPanama, Republic ofPapua New GuineaParaguay, Republic ofPeru, Republic ofPhilippines, Republic of thePitcairn IslandPoland, Polish People's RepublicPortugal, Portuguese RepublicPuerto RicoQatar, State ofReunionRomania, Socialist Republic ofRussian FederationRwanda, Rwandese RepublicSamoa, Independent State ofSan Marino, Republic ofSao Tome and Principe, Democratic Republic ofSaudi Arabia, Kingdom ofSenegal, Republic ofSerbia and MontenegroSeychelles, Republic ofSierra Leone, Republic ofSingapore, Republic ofSlovakia (Slovak Republic)SloveniaSolomon IslandsSomalia, Somali RepublicSouth Africa, Republic ofSouth Georgia and the South Sandwich IslandsSpain, Spanish StateSri Lanka, Democratic Socialist Republic ofSt. HelenaSt. Kitts and NevisSt. LuciaSt. Pierre and MiquelonSt. Vincent and the GrenadinesSudan, Democratic Republic of theSuriname, Republic ofSvalbard & Jan Mayen IslandsSwaziland, Kingdom ofSweden, Kingdom ofSwitzerland, Swiss ConfederationSyrian Arab RepublicTaiwan, Province of ChinaTajikistanTanzania, United Republic ofThailand, Kingdom ofTimor-Leste, Democratic Republic ofTogo, Togolese RepublicTokelau (Tokelau Islands)Tonga, Kingdom ofTrinidad and Tobago, Republic ofTunisia, Republic ofTurkey, Republic ofTurkmenistanTurks and Caicos IslandsTuvaluUganda, Republic ofUkraineUnited Arab EmiratesUnited Kingdom of Great Britain & N. IrelandUruguay, Eastern Republic ofUzbekistanVanuatuVenezuela, Bolivarian Republic ofViet Nam, Socialist Republic ofWallis and Futuna IslandsWestern SaharaYemenZambia, Republic ofZimbabwe

Read more:

Global Marketing Automation Market Report 2021-2026 - Integration of Artificial Intelligence (AI) is Anticipated to Drive the Market -...

January 24, 2022 - Neurobiologists at Harvard University have entered a joint research agreement with NTT Research, Inc., a division of NTT, to study animal neuro-responses with the hope of informing future artificial intelligence systems. The five-year research project, launched in the fall of 2021, enables researchers at the two organizations to collaboratively study how animals maintain behavioral flexibility, specifically in the task of navigation. Greater understanding of how this challenge is approached in biology may eventually enable the design of new computing machines with similar capabilities. The agreement was coordinated by Harvard Office of Technology Development.

The principal investigator is Venkatesh Murthy, PhD, the Raymond Leo Erikson Life Sciences Professor of Molecular and Cellular Biology at Harvard and the Paul J. Finnegan Family Director of the Center for Brain Science. Murthys counterpart at NTT Research for the joint project is Physics & Informatics (PHI) Lab Research Scientist Gautam Reddy, PhD, who was previously an Independent Post-Doctoral Fellow at Harvards NSF-Simons Center for Mathematical and Statistical Analysis of Biology.

This joint research aims to better elucidate how animals maintain the ability to respond appropriately to a wide variety of complex real-world scenarios. The investigators expect the results from one aspect of the research to be a source of new, biologically inspired ideas for artificial reinforcement learning systems that rely on representation learning. Such ideas have played a major role in recent advances in artificial intelligence. Results from another aspect of the research should provide a quantitative understanding of how animals track trails, as well as identify the basic elements of general behavioral strategies that perform flexibly and reliably in the real world. Murthys lab has a long track record in experimental and computational neurobiology. Expertise relevant to the joint research includes the ability to record from or image many individual neurons in the brain while an animal performs behavioral tasks. This technical expertise will enable the research team to understand what computations are performed by biological neural networks when an animal is navigating in a complex world.

Murthy and Reddy have previously worked together on understanding the computational principles behind olfaction. Their focus was on how the smell receptors in the nose respond to blends of odorous compounds. During his time at Harvards NSF-Simons Center for Mathematical Biology, Reddy worked on the theory behind how animals track scent trails and on developing a computational framework to explain how evolution optimizes organisms.

I am delighted to continue this line of inquiry with Dr. Reddy through the NTT Research PHI Lab, Murthy said. The brain is an example of an extremely efficient computational device, and plenty of phenomena within it remain unexplored and unexplained. We believe the results of these investigations in neurobiology will reveal basic understandings and prove useful in the field of artificial intelligence.

Efficient computation is at the heart of quantum computing and neuroscience. Inspired by neuroscience, recent advances in machine learning have recently begun to change how we process data, said NTTs PHI Lab Director Yoshihisa Yamamoto, PhD. This joint research project could provide a rich source of animal-inspired algorithms that generalize across various research domains within NTT and inspire truly novel interdisciplinary ideas.

Adapted from a press release by NTT Research.

Originally posted here:

Collaboration with NTT Research to advance computational neurobiology - Harvard Office of Technology Development