Monthly Archives: July 2021

TOKYO -- Artificial intelligence is transforming the landscape of drug discovery and development. The technology is helping to slash the time and money needed to develop new drugs for COVID-19 and other serious diseases by quickly identifying promising drug candidates.

In the case of COVID-19, the application of AI helped one company come up with a treatment that was approved in the U.S. in a lightning-fast nine months.

British AI startup BenevolentAI identified baricitinib, a drug developed by Elli Lily for the treatment of rheumatoid arthritis, as a potentially effective COVID-19 drug in just a few days. The medication has been approved as a COVID-19 treatment in the U.S. and Japan. The European Medicines Agency has also begun evaluating baricitinib for use against the coronavirus.

A BenevolentAI specialist team was tasked with using the company's state-of-the-art AI to find an already approved drug that could be repurposed as a COVID-19 treatment. This approach made it possible to win emergency use authorization by the U.S. Food and Drug Administration to treat hospitalized COVID-19 patients in just nine months, instead of the several years typically required. Drug discovery usually involves a long search for candidates and animal testing to evaluate their safety.

BenevolentAI's technology identifies potential drug candidates using data from clinical trials, academic papers, and its own database on diseases, genes and pharmaceuticals. When a target protein is identified, AI finds candidate drugs that act on it.

Applying AI to drug discovery and development is expected to sharply reduce in the time required to create new drugs, a process that usually takes nine to 17 years. That time could be cut in half for approved drugs that are repurposed for other uses.

In February 2020, soon after the World Health Organization declared the COVID-19 outbreak to be a public health emergency of international concern, BenevolentAI's first paper on baricitinib as a candidate COVID-19 treatment, published in the British medical journal The Lancet, found that the drug may inhibit the ability of the virus to infect lung cells and cause inflammation in patients.

Eli Lilly, which owns the rights to baricitinib, and the National Institute of Allergy and Infectious Diseases launched a study in the U.S. to examine the efficacy and safety of the drug as a potential treatment for hospitalized COVID-19 patients. Because the study found that baricitinib can shorten recovery times and improve clinical outcomes for patients, the FDA granted emergency use authorization for the drug in November last year. The drug has been shown to reduce mortality in hospitalized patients by 38% when used in combination with remdesivir, an antiviral medication, according to data released by Eli Lilly.

BenevolentAI is also developing drugs on its own, focusing on treatments for more than 10 diseases, including atopic dermatitis and amyotrophic lateral sclerosis (ALS), also known as motor neurone disease or Lou Gehrig's disease.

The company started a clinical trial on a potential treatment for atopic dermatitis in February. It is also working with AstraZeneca to develop a treatment for chronic kidney disease.

Use of AI in drug discovery and development is spreading around the world. Sumitomo Dainippon Pharma, in partnership with Exscientia, an Oxford, England-based AI drug discovery startup, has found a candidate treatment for obsessive-compulsive disorder. Last year, the Japanese drugmaker began clinical trials of the drug candidate in Japan to evaluate its safety.

"We found the candidate in less than a year using AI for the process, which typically takes four and a half years," said an executive at Sumitomo Dainippon Pharma. In May, the company started Phase 1 clinical trials in the U.S. on an Alzheimer's disease psychosis drug candidate designed using Exscientia's AI technology.

AI does not do all the work. It is used to find candidate drugs and narrow down the design of new drugs by crunching huge amounts of data from scientific papers and experiments. People must work out which direction to take the research and development itself.

Exscientia is attracting the attention of pharmaceutical and biotechnology companies around the world. Evotec, a German biomedical company, has jointly developed a new cancer treatment with Exscientia. Human clinical trials of the A2a receptor antagonist began in April, according to Evotec. The candidate drug was discovered eight months after the two companies launched the project.

Taisho Pharmaceutical of Japan and Insilico Medicine, a Hong Kong-based AI startup, started a joint research project last fall to identify therapeutic compounds that may slow the cellular effects of aging. Insilico is using its AI networks to identify therapeutic targets and find druglike molecules that target senescent cells. The accumulation of these cells as people age is thought to be behind a variety of diseases.

Insilico's job is to identify the role senescence plays in specific cells, tissues and diseases, with different proteins implicated for each, and to design molecules to tackle those targets. Taisho will validate the computer-generated compounds through in vitro and in vivo testing.

Some Japanese AI startups are playing catch-up with such overseas leaders in the field. Hacarus, a Kyoto-based AI startup, and the University of Tokyo in June announced the start of a joint research project to develop cures for Alzheimer's disease and Parkinson's disease. Both are caused by the accumulation of certain proteins in the brain. Using AI to develop drugs for these types of disease is still rare.

The project is aimed at creating a system in one year to efficiently search for compounds that could be candidate drugs. The AI-driven approach will "dramatically improve the speed and accuracy of the research process, which has traditionally depended on human hands and eyes," said Taisuke Tomita, a professor at the University of Tokyo's Graduate School of Pharmaceutical Sciences.

More than 120 Japanese companies and universities have joined the Life Intelligence Consortium, an industry-academia collaboration aimed at applying AI to the life sciences. The consortium has already created around 20 prototype AI programs for drug discovery and development.

"AI will soon become an essential technology for drug discovery and development," said Yasushi Okuno, a Kyoto University professor. The technology offers the opportunity to "reconsider the conventional wisdom that developing a new drug takes 10 years."

Coming up with new has become hugely expensive. The average cost to develop a prescription drug that makes it to market soared to some $2.9 billion in the 2000s, up from $180 million in the 1970s, according to an estimate by Tufts University in the U.S. The rise in drug development costs has been far steeper than inflation overall.

Many new drugs have been developed over years, especially treatments for cancers and "lifestyle diseases." Many substances that act on important molecules implicated in the development of diseases have already been identified and developed into drugs. As a result, it is becoming increasingly difficult to develop effective new drugs. And a greater emphasis on safety has stretched out the time needed for clinical trials.

Only one in about 30,000 candidate substances actually becomes a new drug, according to the Japan Pharmaceutical Manufacturers Association. The process can take nine to 17 years. R&D spending by pharmaceutical companies is equal to roughly 10% of annual sales, compared with around 4% for the manufacturing sector as a whole.

In their efforts to come up with new treatments, drug companies are devoting more of their resources to the development of biopharmaceuticals -- complex medicines made from living cells or organisms, often created using cutting-edge technologies, including antibody drug conjugates. Developing and manufacturing biopharmaceuticals is thus complicated and costly.

One such drug, Nivolumab, is sold under the brand name Opdivo. It was first used in Japan in 2014 to treat a variety of cancers and initially cost some 35 million yen ($318,000) a year to administer. This led to complaints that its use would further strain the government's already huge and growing health care spending.

AI is among the technologies that could, by drastically shortening drug development time and cost, help to keep prices in check, thereby improving access to new treatments for previously intractable diseases and enhancing the quality of life for everyone.

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AI slashes time and cost of drug discovery and development - Nikkei Asia

Women are shaping AI advancement in federal IT and helping to remove biases in data sets.

Womenin tech can supercharge teams' creativity and help them stay under budget, meet deadlinesand improve outcomes, studies show, so its time for more women to pursuetech careers, according to a lead Department of Labor officialspeaking at GovernmentCIOMedia & Researchs Women Tech Leaders eventThursday.

Kathy McNeill, who leads emerging technology strategy at the agency,said the federal government needs more women in AIto produce accurate data sets and data analysis.

AI is a reflection of those who develop it and the data sets we use, she said during a fireside chat.

McNeill provided an example of how Google Translate took the phrase she is a doctor and he is a babysitter and translated it to he is a doctor and she is a babysitter in another language, to illustrate biases inherent inartificially intelligent algorithms.

A lot of systems were developed 10 to 20 years ago, she said. Think how we've evolved and changed since then. Some of our government systems are even older than that think of the biases that must exist in those systems. There are data sets we use today that were developed in the 60s that had women tagged as homemakers when in fact they were teachers, or scientists, or lawyers. We need women, and we need women of diverse backgrounds to make sure we're doing real technical work to minimize the biases in systems.

According to a2015 American Association of University Womenstudy, the number of women in tech careers dropped from35% in 1990 to 26% in 2013.

McNeill believes one reason for this is because womencan be questioned and treated with skepticism.

Tracey Cho, she's a rock star in Silicon Valley, she's got technical chops at Google, Pinterest and Facebook, McNeill said. She was quoted in an article in 2019 ...talking about computers and computer science. She said women are still questioned for their technical chops and treated skeptically, sometimes straight out [with] hostility.We need to create a culture that's inclusive for women.

Technology jobs in government are chronically underfilled, and with more women pursuing STEM education than any time in history, now is the time to pursue a career in federal IT, McNeill said. Women are already shaping AI advancement at federal agencies in significant ways.

Krista Kinnard, division director of technology at the Department of Labor, is pioneering use of AI to help screen candidate resumes for human resources, improve compliancetraining and boost cybersecurity controls.

In government we have dimensional challenges, McNeillsaid. When it comes to AI andmachine learning and modern technology, we have a mix of aging and new technology, [so] we have very complex problems to solve.We're advancing so rapidly, there are so many ways to get involved. We, as women, bring such a rich perspective to the tech team.

McNeill advised women in tech or interested in tech careers to find a mentor, be a mentor, and be a lifelong learner.

It's also important to be true to who you are, she added. Always look for ways to learn about the newtechnologies and solutions. We put a lot of pressure on ourselves to get everything right. You're not going to get everything right. So be agile, start fast, fail, learn, and iterate. Speak up, volunteer for the tough project, the new position. Be tenacious. Don't take no for an answer.

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Why Government Needs More Women in AI - GovernmentCIO Media & Research

On Tuesday, Jeff Bezos and three passengers reached the edge of space and safely returned Tuesday morning after a flight of just over 10 minutes that the billionaire businessman hopes will kick-start an expansive new era for human space travel. What were doing is the first step of something big. Big things start small, Bezos said. Tuesdays space flight was Blue Origins first with passengers on board. Bezos was accompanied by his brother Mark, a 82-year-old aviator Wally Funk and 18-year-old student Oliver Daemen.

Founded 2000, Blue Origin currently employs more than 3,500 people spread over Florida, California and other locations. Blue Origin and Virgin Galactic have been applauded for successfully launching sort of worlds first commercial astronauts. But, looks like those laurels have to wait for a while.The Federal Aviation Administration (FAA) spoiled the party by releasing new rules on the day of Bezos historic flight. According to the new rules, to qualify as commercial astronauts, space-goers must travel 50 miles (80km) above the Earths surface and must have demonstrated activities during flight that were essential to public safety, or contributed to human space flight safety.

Alphabets Moonshot Factory X has launched a new robotics company, Intrinsic, which develops software and AI tools that use sensor data from a robots environment to learn from, and quickly adapt to the real world. The team at Intrinsic features a diverse group of talent from robotics, film production, design, computer perception, and mechanical design.

Founded in 2010 by Google founders Larry Page and Sergey Brin, X was established with the goal to work on moonshots: far-out, sci-fi sounding technologies that could one day make the world a radically better place. So far, X has incubated hundreds of different moonshot projects in fields ranging from computational agriculture to cybersecurity; from seawater fuel to machine learning and more.

On Friday, NASA announced that it has awarded a new contract to Elon Musks SpaceX to provide launch services for Earths first mission to conduct detailed investigations of Jupiters moon Europa. The Europa Clipper mission, which will launch in October 2024 will use SpaceXs Falcon Heavy rocket. The total contract award amount for launch services is approximately $178 million.

Europa Clipper will conduct a detailed survey of Europa and use a sophisticated suite of science instruments to investigate whether the icy moon has conditions suitable for life. Key mission objectives are to produce high-resolution images of Europas surface, determine its composition, look for signs of recent or ongoing geological activity, measure the thickness of the moons icy shell, search for subsurface lakes, and determine the depth and salinity of Europas ocean, said NASA in a statement.

On Wednesday, the Federal Trade Commission voted to enforce laws around the Right to Repair, thereby ensuring that US consumers will be able to repair their own electronic and automotive devices. Right to repair initiatives would require the smartphone, laptops and other device makers to append their products with a how to repair manual to assist their customers. And would establish more transparency with regards to green initiatives and also restrict the makers from resorting to planned obsolescence. This resolution might also encourage responsible marketing and advertising.

At the 2021 International Conference on Machine Learning (ICML), DARPA, IBM, MIT and Harvard released a new dataset for benchmarking AI intuition, along with two machine learning models representing different approaches to the problem. According to the researchers, this work aims to accelerate the development of AI that exhibits common sense. These tools rely on testing techniques that psychologists use to study the behavior of infants and is an extension of neuro-symbolic AI research, which combines the logic of symbolic AI algorithms with the deep learning capabilities found in neural networks.

Steve Jobs is out there challenging the status quo yet again. Jobs job application is being auctioned and this is the first time an item is available both in physical as well as NFT format for auction. Dated 1973, this artifact will go out in only one form either physical or digital based on the highest bidder and their choice of format. Ever since its introduction, NFTs(non-fungible tokens) have been a rage, fetching as high as $60 million for artwork. But, in the case of Jobs application, the interest for physical format has surpassed the NFT cult by a huge margin. The auction, which is going to end in four days time, is a key litmus test for the future of decentralised collectibles.

Ubers trucking division, Freight, has spent $2.25 billion to acquire Transpalce, a company that makes shipping software. According to reports, Uber Freight will acquire Transplace from TPG Capital, the private equity platform of alternative asset firm TPG that acquired Transplace in 2017.Uber has recently shut down its AI research labs and currently focussing on investing in talent houses that can boost its freight economy.

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Bezos Makes History, Common Sense AI And More In This Week's Top News - Analytics India Magazine

July 23, 2021 by Ken Chiacchia, Pittsburgh Supercomputing Center/XSEDE

AI poses great potential for improving productivity in the technology workplace, Salwa Alamir of JPMorgan Chase said in her plenary session at PEARC21. A data-driven ML approach can help identify worker skills necessary for a job or a role in a given project, determine how projects can be sped up via improved task management and boost understanding of how code can be better maintained. ML- and data-driven analyses of three areas people, tooling and process have shown promise in improving the productivity of software developers, with further applications across that vast banking enterprise.

The PEARC conference series provides a forum for discussing challenges, opportunities and solutions among the broad range of participants in the research computing community. This community-driven effort builds on successes of the past, and aims to grow and be more inclusive by involving additional local, regional, national and international cyberinfrastructure and research computing partners spanning academia, government and industry. PEARC21, Evolution Across All Dimensions, was offered this year as a virtual event (July 19-22).

Tooling: Data Driven Approach

JPMorgan Chase employs more than 50,000 technologists, with an annual budget of more than $11 billion, Alamir said. The AI team began by asking whether the companys productivity-to-cost balance could be improved via machine learning. They devised a three-pronged approach that split the problem into three productivity enablers: people, process and tooling.

For the tooling analysis, the team analyzed the correlation between productivity tool adoption with industry standard metrics such as lead time for changes, change failure rates, deployment frequencies and mean time to recovery after failures.

Salwa Alamir of JPMorgan Chase

We decided to use some of these metrics to decide what are the most useful tools developers can use, she said. We decided that unique incidents are not as bad as incidents that are happening over and over, given that the latter imply developers may be addressing the symptoms and not the root causes of a given incident. They identified a number of tools associated with better metrics, including continuous integration/continuous delivery tools, cloud tools, training portals and task-management tools that the company should prioritize use of by its developers.

People: AI for Skills Understanding

For people, we were interested in skill sets, she said. How can we identify skill sets and retain the top talent we have in the firm? The focus of the exercise was automated review of resumes.

At any time, JPMorgan Chase has numerous job positions open globally. The number of resumes it receives for these positions is on the order of a million; manual review of these resumes is often time-prohibitive. Aids to automate the process to date, such as keyword searches, have proved inadequate to the task.

Resume formats also proved a challenge, Alamir reported. For example, a resume written in columns cant be read line by line, or the AI risks conflating information pertaining to different topics. Skills are changing over time, so no static list of required skills will remain relevant for long the AI would have to detect new skills not present in the training data.

The team hit upon a four-dimensional rubric, featuring a deep soft-skill extractor, a sectional hard-skill extractor, a project-delivery extractor and an experience extractor designed to extract skills from any free text. While intended for situations of applicant surplus, they found that their algorithms could also be applied to the opposite problem of too few applicants by searching for skills that are relevant for other open positions.

Process: AI for Task Management

The process productivity enabler portion of the effort focused in part on project management. The team developed a technique able to detect patterns of mismanagement among projects. These patterns may include the cliff, in which the projects languish for long periods and then the work is performed at the last minute; the gap, in which the project proceeds according to schedule for a time but then enters a phase in which the remaining work isnt completed; and the wave, in which successive bursts of task completion and addition of new tasks kept the project from reducing the amount of outstanding work. Notably, employees report that task planning and management was time consuming, with sudden workloads posed by unexpected events.

To improve on the current, manual planning method, Alamir and her colleagues leveraged historical records kept at JPMorgan Chase that tracked every development task ever created at the company. Learning from these data, their algorithm considers task difficulty, priority, precedence, duration, developer skills and additional constraints specific to a particular project to assign tasks optimally and minimize time to completion.

The team has a paper on that work that has been accepted for publication. Moreover, an informal survey of the companys development teams elicited positive reviews of the manageability of the AI-generated project plans.

Process: AI for Code Maintenance

Another aspect of the process study was that of the vast network of scripts underlying the companys systems. Some of its code repositories are huge, containing more than 10 million lines created by thousands of developers. Development can be accelerated when common tasks are performed by the same piece of code in the repository. But this approach poses risks.

It causes a dependency on scripts that developers may not be aware of, Alamir said. If a developer decides to change a function, [they] dont know how many people are using it. It may happen that ten teams [code] fail because of that change. Uncovering and correcting those failures manually can take months.

The AI team developed a multistep approach to solve the issue. By generating a network graph through static analysis of the code repository, they were able to visualize the directional code dependencies in a given program. By parsing release notes they were able to find deprecated functions requiring updates. The AI is able to automate that process when an update is available, or alert developers when an updated script isnt available at a high degree of confidence and the code must be corrected manually. Finally, unit tests of the updated scripts and scripts with dependencies can confirm a successful update.

The J.P. Morgan team has to date not implemented their algorithms at a scale large enough to demonstrate improvement in the three productivity enablers. Alamir is confident, though, that the size of the enterprise will greatly multiply even modest gains.

One hour more productivity per week per developer in such a large company will add up, she said. Were applying economies of scale; even just a little bit of improvement could end up being huge.

Read additional coverage of PEARC21 on our sister site HPCwire.

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About the author: Tiffany Trader

With over a decades experience covering the HPC space, Tiffany Trader is one of the preeminent voices reporting on advanced scale computing today.

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How AI Is Transforming the Technology Workplace: Lessons from JPMorgan Chase - EnterpriseAI

A University of Oklahoma researcher and a surgeon at OU Health, based in Oklahoma City, had a vision of using AI to visualize superimposed and anatomically aligned 3D CT scan data during surgery. The mission was to augment every surgery.

THE PROBLEM

"Compared to a pilot flying a plane or even a regular Google Maps user on his way to work, surgeons today have their instruments clustered behind them hanging on the wall," said Mohammad Abdul Mukit, an MS student in electrical and computer engineering at the University of Oklahoma, and a graduate fellow and research assistant. His research focuses on applications of computer vision, extended reality and AI in medical surgeries.

"The Google Maps user or the pilot gets constant, real-time updates regarding where they are, what to do next, and other vital data that helps them make split-second decisions," he explained. "They don't have to plan the trip for days or memorize every turn and detail of every landmark along the way. They just do it."

On the other hand, surgeons today have to do rigorous surgical planning, memorize the specifics of each unique case, and know all the necessary steps to ensure the safest possible surgery. Then they engage in complex procedures for several hours, with no targeting or homing devices or head-mounted displays to assist them.

"They have to feel their way to their objective and hope everything goes as they planned," Mukit said. "Through our research, we aim to change this process forever. We are making the 'Google Maps for surgery.'"

PROPOSAL

To turn this vision into reality, Mukit and OU Health plastic and reconstructive surgeon Dr. Christian El Amm have been working together since 2019. This journey, however, started in 2018, with El Amm's collaboration with energy technology company Baker Hughes.

BH specializes in using augmented reality/mixed reality and computed tomography scans to create 3D reconstructions of rock specimens. For geologists and oil and gas companies, this visualization is extremely helpful as it assists them to efficiently plan and execute drilling operations.

Mohammad Abdul Mukit, University of Oklahoma

This technology caught the attention of El Amm. He envisioned that this technology combined with AI could allow him to visualize superimposed and anatomically aligned 3D CT scan data during surgery. This could also be used to see reconstruction steps he had planned during surgery while never losing sight of the patient.

However, several key challenges needed to be solved to get a prototype mixed reality system ready for use in surgery.

MEETING THE CHALLENGE

"During the year-long collaboration, the BH team created solutions for those challenges that, until that time, were unsolved," Mukit recalled. "They implemented a client/server system. The server a high-end PC equipped with RGBD cameras would do all the computer vision work to estimate the six DoF pose of the patient's head.

"It would then stream the stored CT scan data to the client device, a Microsoft Hololens-1, for anatomically aligned visualization," he continued. "BH developed a proprietary compression algorithm that enabled them to stream a high volume of CT scan data. BG also integrated a proprietary AI engine to do the pose estimation."

This was a complex engineering project done in a very short time. After this prototype was completed, the team had a better understanding of the limitations of such a setup and the need for a better system.

"The prototype system was somewhat impractical for a surgical setting, but it was essential for better understanding our needs," Mukit said. "First, the system couldn't estimate the head pose in surgical settings when most of the patient's body was covered in clothing except the head. Next, the system needed time-consuming camera calibration steps every time we exited the app.

"This was a problem since according to our experience, surgeons accept only those devices that just work from the get-go," he continued. "They don't have the time to fiddle around with technology while they are concentrating on life-altering procedures. We also deeply felt the need for the options to control the system via voice commands. This is an essential element when it comes to surgical settings as the surgeons will always have their hands busy."

Surgeons will not be contaminating their hands by touching a computer for controlling the system or by taking off the device for recalibration. The team realized that a new, more convenient and seamless system was essential.

"I started working on building a better system from scratch in 2019, once the official collaboration ended with BH," Mukit said. "Since then, we have moved most of the essential tasks to the edge, the head-mounted display itself. We also leveraged CT scan data to train and deploy machine learning models, which are more robust in head pose estimation than before.

"We developed 'marker-less tracking,' which allows the CT scan or other images to be superimposed using artificial intelligence instead of cumbersome markers to guide the way," he added. "We then eliminated the need for any manual camera calibration."

Finally, they added voice commands. All these moves made the apps/system plug-and-play for surgeons, Mukit said.

"Due to their convenience and usefulness, the apps were very warmly welcomed by the OU-Medicine surgeons," he noted. "Suddenly ideas, feature requests, queries were just pouring in from different medical experts. I realized then that we had something really special in our hands and that we had only scratched the surface. We started developing these features for each unique genre of surgery."

Gradually, this made the system enriched with various useful features and led to unique innovations, he added.

RESULTS

El Amm has begun using the device during surgical cases to enhance the safety and efficiency of complex reconstructions. Many of his patients come to him for craniofacial reconstruction after a traumatic injury; others have congenital deformities.

Thus far, he has used the device for several cases, including reconstructing a patient's ear. The system took a mirror image of the patient's other ear, then the device overlaid it on the other side, allowing El Amm to precisely attach a reconstructed ear. In the past, he would cut a template of the ear and aim for precision using the naked eye.

In another surgical case, which required an 18-step reconstruction of the face, the device overlaid the patient's CT scan on top of his real bones.

"Each one of those bones needed to be cut and moved in a precise direction," El Amm said. "The device allowed us to see the bones individually, then it displayed each of the cuts and each of the movements, which allowed the surgeon to verify that he had gone through all those steps. It's basically walking through the steps of surgery in virtual reality."

ADVICE FOR OTHERS

"When you change the way you see the world, you change the world you see," Mukit said. "That is what mixed reality was made for. MR is the next general-purpose computer. Powerful technology will no longer be in your pockets or at your desks.

"Through MR, it will be integrated with your human self," he continued. "It will change how you solve problems, which in turn will lead to new creative ways of solving problems with AI. I think that within the next few years we are going to see another technology revolution. Especially after a mixed reality head-set is unveiled in 2023, which is reported to be lighter than any other visors in the market."

Currently, almost every industry is integrating mixed reality headsets into their businesses rightly so, as the gains are evident, he added.

"This technology is now mature enough for countless possible applications in almost every industry and especially in healthcare," he concluded. "Mixed reality has not made its way fully into this industry yet. We have only scratched the surface, and already in a few months, we have seen such an overwhelming tsunami of ideas from experts. Ideas that now can be implemented with ease.

"These application scenarios range from education and training to making surgeries safer, faster and more economical for both the surgeons and patients. The time to jump into mixed reality is now."

Twitter:@SiwickiHealthITEmail the writer:bsiwicki@himss.orgHealthcare IT News is a HIMSS Media publication.

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Surgeon and researcher innovate with mixed reality and AI for safer surgeries - Healthcare IT News

With Battlefield 2042 confirmed to feature AI bots (that cant be turned off) to make sure servers stay full constantly, some might be wondering how these Battlefield 2042 AI bots will react in-game. As it turns out, very well, it looks like at least according to Ripple Effect Studios, whos co-developing Battlefield 2042 alongside the main studio over at DICE Sweden.

Talking about the difficulty of the AI in Battlefield 2042s Battlefield Portal modem Design Director Justin Wiebte of Ripple Effect Studios states that players will have a really hard time telling the difference between AI bots and real human players.

Weve tried to put a lot of effort into making them play just like a player would. So it would be really hard for people to tell the difference between an AI and a real human player because they will run around, they will drive vehicles, they will pick each other up, they will drop [each other] off at objective locations and things like that its a very intelligent system. And then you can tweak and tune some of the things they are and arent allowed to do, including how difficult they are to play against.

This is very encouraging to hear. If the AI bots are really this good in-game, I suspect some players would rather have these as squad mates rather than human players, since some of the latter fail at doing the most basic of team-oriented tasks such as giving ammo, healing, and the like. That said, given how Battlefield 2042 AI bots are confirmed to not be able to use Specialties and Traits, they are severely limited in what they can do.

Source: VG247

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Battlefield 2042 AI Bots Will Be "Really Hard" to Tell Apart From Humans Says Devs - MP1st

A buzzword for the past few years, artificial intelligence (AI), is changing not only the civilian world but militaries and battlefields across the globe, with the Israel Defense Forces at the forefront.

The IDF has been working on AI for decades after troops and officers first recognized the need and realized that the military and defense establishment had to invest time and manpower in the development of the technology.

The world has changed; we are living in a world full of data, Maj. M., a senior officer in the C4I Directorate told The Jerusalem Post as we sat in his office in a nondescript base in central Israel.

In the small base with old buildings, his office is full of plaques and awards for his unit, which has been at the forefront of the IDFs digital revolution.

Since 2005 the technology has made a revolution in the military. Its allowed us to acquire a lot more intelligence with a lot more velocity.

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With battlefields changing, a central part of the IDFs Momentum multiyear plan is to transform the IDF into a smart army, holistic and tech-friendly, using simulators for more and more battalions and using AI to significantly increase its target bank.

The IDF is now data-driven, Maj. M. said, adding that its no small challenge to take the data, use algorithms to analyze them, and get them to the troops on the front lines.

While the Israeli military relied on what was already on the civilian market and adapted it for military purposes, in the years before the fighting, the IDF established an advanced AI technological platform that centralized all data on terrorist groups in the Strip on one platform that enabled the analysis and extraction of the intelligence.

For the first time, a multidisciplinary center was created that produces hundreds of targets relevant to developments in the fighting, allowing the military to continue to fight as long as it needs to with more and more new targets, a senior officer said at the time.

While the IDF had gathered thousands of targets in the densely populated coastal enclave over the past two years, hundreds were gathered in real time thanks to programs developed by soldiers in Unit 8200 who pioneered algorithms and code.

Troops from Maj. M.s unit were sent to the Gaza Division to help soldiers and commanders understand all the data they had been given.

The military believes that using AI helped shorten the length of the fighting, having been effective and quick in gathering targets using super-cognition.

And in the North, using innovative intelligence and advanced technology, the IDFs target bank in the Northern Command is 20 times larger than the target bank the military had in 2006, with thousands of targets ready to be attacked, including headquarters, strategic assets and weapons storehouses.

In addition to AI being used to gather intelligence and targets, the IDF is also using more robotic platforms and drones along its borders.

We want our borders to be smart and deadly. Instead of putting troops at risk, we can deploy a semiautonomous vehicle with sensors and cameras to do the same job, Maj. M said. But theres always a person sitting in the command room operating it.

And while troops did not maneuver inside Gaza during the fighting in May, future battlefields will see troops on the front lines with all the data they need in real time, such as what weapons they need to hit targets, who will support them, and more Maj. M. said.

Along with the IDF, Israeli defense companies such as Israel Aerospace Industries and Rafael Advanced Defense Systems have also been pioneering AI technology for years.

ITS BEEN the buzzword for the past few years; everyone is using it, Dr. Irit Idan, executive VP, R & D, Rafael, told the Post.

While only recently has it been in the spotlight, AI has been around since the 1950s, Idan explained.

Its not something new, but there are waves where we see several jumps in capabilities, she said.

The first wave, from the 1950s to the 2000s, laid down the rules that are still being used today, but intelligence was gathered in a fairly simple manner. The second wave, from 2000 to 2020, dealt with machine learning and statistical intelligence, but the machine was unable to explain how it arrived at a connection or answer.

Idan said that we are now in the third wave, where companies want the machine to explain the rules that it is using to make the decision.

Its very important to be able to explain the decision-making; because if you want to rely on the machines decision and base your action on AI, you have to really understand why it says what it says, she said, adding that its very important for the civilian market, but even more important in the defense industry and on the battlefield.

With a lot of challenges remaining in this third wave, Idan told the Post that there are some places where there are no humans involved, although people will still need to be in the loop for most decisions.

Using the examples of two well-known Rafael systems that use AI, the Iron Dome and Windbreaker, Idan said that time is a crucial aspect.

A shell launched towards a tank from a short distance and the system needs to identify the launch, what kind of shell was launched, and destroy it within a few seconds. No human brain can do that in the few seconds that you have between the launch and the hit, she said. We have to rely on the AI in the system.

But with the Iron Dome theres a bit more time involved, and therefore a human is involved in the decision-making process.

And thats what we are going to see in the coming year, where man and machine work together and know their strengths and weaknesses and how to get the best result, Idan added.

We have our hands on the pulse on whats going on across the world in AI, Idan said, explaining that Rafael uses AI for both civilian and military needs.

Pointing to companies like Google, Amazon and Facebook, Idan said that the AI market is worth billions of dollars, and it is all based on data. And whoever has the data leads AI.

Citing the examples of China and Russia, Maj. M said that the IDF wants AI superiority; we want to be quicker, more precise, effective, and not at a high cost.

According to Idan, while Israel is a groundbreaker in the field, China is a leader in AI because there are no regulations in China when it comes to such technology.

Russia is also at the forefront, and in 2017 Russian President Vladamir Putin said that artificial intelligence is the future, not only for Russia but for all humankind.... Whoever becomes the leader in this sphere will become the ruler of the world.

As technology continues to break barriers, AI will continue to be the buzzword, both in the military and in civilian spheres around the world.

And Israel and the IDF will continue to aim for AI superiority.

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