Category Archives: Artificial Intelligence

LinkedInGuru, founded by Canadian entrepreneur Aaron Vasant, helps job seekers thrive in the highly competitive job market using AI advancement and modern design strategies.

TORONTO(BUSINESS WIRE)The Guru team is making waves in the resume writing industry through integrating artificial intelligence and design into their process to optimize the credibility of clients resumes and increase their chances of landing an interview.

Aaron, Founder and CEO of LinkedInGuru, explains, With the introduction of applicant tracking systems and keyword scanners, the front end or early stages of the recruitment process is now almost never done by the human eye. Only nine Fortune 500 companies dont use bots or an ATS system to review resumes before passing them on.

ATS can scan and filter those who possess specific keywords and skills using data-driven recruitment software. Quite often, upwards of 75% of applicants do not make it past the initial screening process.

Aaron decided to create the company after making a variety of remarkable discoveries throughout the course of his MBA. His research led him to discover the core functionality behind the applicant tracking systems, which he has used in developing LinkedInGurus strategy and success formula.

If your resume makes it past the bots, eventually, a human will read it. This is where Aaron acknowledged that the content and design must be captivating so they want to learn more about you. You must strike the perfect balance between keywords, design, and content to have the best resume possible.

LinkedInGurus proprietary method has seen a lot of success, citing that 94% of their clients have received an interview within 60 days of purchasing their service. The company is trusted by professionals at Amazon, Tesla, Walmart, Google, CNN, Adidas, Microsoft, and more!

The process is pretty simple, you begin by answering their standardized recruitment questionnaire, then select your favourite ATS friendly template from their proven library, and the draft will be completed within 5-7 business days.

Contacts

Media Contact:

Name: Aaron Vasant

Company: LinkedInGuru

Email: support@linkedinguru.caWebsite: LinkedInGuru.ca

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LinkedInGuru Innovates Resume Writing by Utilizing Artificial Intelligence and Design - TechDecisions

The intersection of between computers, neurotechnologies, and the human brain.

This past week, Elon Musks new venture Neuralink made headlines by showing a video of a monkey playing Pong with his mind, controlled by a surgically implanted wireless device that can directly read brain signals and interpret its intended commands. The technologies that enable such communication between a computer and the brain are called brain-machine interfaces (BMIs).

Brain-machine interfaces - or brain-computer interfaces, the terms are used interchangeably - are technologies designed to directly plug into the nervous system: the brain, retinas in the eyes (which are actually a part of the brain itself), spinal cord, or peripheral nervous system. The Neuralink example and other similar technologies are designed to read and decode neural signals from individual neurons in selected parts of the brain in an attempt to understand the brains outputs. Instead of the outputs going to the arm of a monkey or human controlling a joy stick to play Pong or some other video game, they go to a computer which plays the game instead.

How do they achieve this? Specially designed electrodes are surgically implanted into a target region of the brain where the neural signals need to be recorded. Those signals are then decoded and the intent of the brain interpreted by mathematical models and computer algorithms that take advantage of what is known about how the brain works. Eventually, the commands interpreted by the computer are used to execute desired functions or tasks, such as controlling a robotic arm, generating synthesized speech, or playing video games.

Because surgically implanted BMIs are highly invasive, their use is restricted to restoring clinical function in patients suffering debilitating neurological disorders, in particular motor disorders such as paralysis following spinal cord injury or stroke, locked-in syndrome, and amyotrophic lateral sclerosis (ALS). The impact these technologies can have on the quality of life of these patients and their families cannot be overstated.

Until relatively recently, surgically implantable BMIs necessitated wired connections between the brain and the computer the wires were plugged into. But this has a number of serious disadvantages and risks. The electrodes can move in unintended ways as mechanical forces are exerted on the wires, and it can lead to a significant risk of infection or other types of injury. More recently though, BMIs implanted in the brain have gone wireless. The entire device is self contained within the skull and brain with no external wires protruding out. They communicate with external computers using various through the air protocols and algorithms in a similar way your Bluetooth and WiFi devices work.

In contrast, non-invasive BMIs are very different from surgically implanted invasive BMIs. Non-invasive BMIs rely on electroencephalography(EEG) and related methods to read and interpret brain waves. They do not require surgically implanted electrodes, but rather external electrodes integrated into form factors a user can wear and take off as needed - like a cap. The video game industry and virtual and augmented reality worlds have a strong interest in non-invasive BMIs, for example. These market segments are one of the main economic drivers for research in this area. Unfortunately though, the resolution and quality of measured brain signals these non-invasive methods provide are generally not sufficient for the needs demanded by clinical applications.

The earliest work using EEG to measure and attempt to make sense of brain signals is over 100 years old, dating back to the 1920s. And the engineering accomplishments behind the press Neuralink has been receiving lately is grounded in years of pioneering work by a number of research groups from around the world. In 2012 researchers from Brown University in Providence, Rhode Island, along with colleagues at Massachusetts General Hospital and Harvard University in Boston, and the Institute of Robotics and Mechatronics in Germany, showed that a wired BMI could successfully be used in human patients with tetraplegia - a severe form of paralysis in all four limbs - to control a robotic arm to drink, and to control a computer screen to read email.

This effort is part of the BrainGate project, a collaborative effort between Brown University, Case Western University, Massachusetts General Hospital, Stanford University, and the Department of Veteran Affairs. In their most recent work, published just a few days ago, the team introduced a wireless version in humans of a previous wired prototype. The patients were able to surf the web and use other apps on a commercially available tablet computer.

In some of the earliest work in the field, researchers at Duke University in 2014 were able to wirelessly record from 1800 distinct neurons in the brains of freely moving monkeys for nearly five years. And in 2016 the same group showed that monkeys implanted with their wireless BMI could use the system to continuously manipulate and drive a wheel chair.

And now, converging with advances taking place in machine learning, BMIs are on the verge of entirely new capabilities.

There is a tremendous amount of engineering that goes into developing BMIs. State of the art micro- and nano-fabrication, mathematical and computer modeling, extensive neurobiological experiments, pre-clinical testing in animal models, and clinical testing in humans all need to take place. Because of the up-front complexity and effort required in building these devices, once it is built and tested the design and engineering details are pretty much fixed. This means that the functionality of the BMI, what it can do and how it operates, is by necessity also fixed and limited to the constraints imposed by its design specifications.

The problem, however, is that the requirements and needs of different patients will vary to significant degrees from individual to individual. Even for patients diagnosed with the same disorder, how the parameters of a BMI are fine tuned may need to be different in order to achieve optimized performance tailored to the individual. For example, how many neurons to record from and how decoding algorithms should interpret changes in recorded signals. And equally, if not more significant, the needs of the individual patient themselves will change and evolve over time as disease progresses or even over time as a normal part of aging.

Even more challenging, how a BMI needs to interact with the brain may vary on relatively short and highly dynamic, i.e. changing, time scales within the course of minutes or hours. For example, depending on the physical nature of an activity a patient is engaged in, or the degree of an intellectual demand associated with a specific task, the BMI may need to quickly adapt. What the brain needs to do to change the channel on the TV is very different than what it needs to do if it is playing a difficult video game, for example.

Even the time of day and cognitive state of the individual may have an effect on the demands put on a BMI. Are you trying to focus on a task late in the evening when you are tired? Or is it the morning and you are fresh and ready to go?

In short: a one-size-fits-all BMI cannot be truly optimized to the needs of an individual patient after it is surgically implanted.

Some BMI technologies already incorporate physiological feedback or patient input to adjust their outputs and functions. But in general, human interaction is needed, such as subjective or perceptual feedback from the patient, or manually adjusting parameters by a doctor. The integration of state of the art machine learning to achieve optimized near real-time functionality in BMIs - in other words, adaptive and autonomous smart BMIs - is still in its earliest stages.

With the integration of machine learning, BMIs may one day be able to learn and anticipate the contextual needs of situations a patient finds themselves in. Such BMIs will be able to adjust their outputs and functions in near real-time to accommodate changing cognitive and physical demands. Or be able to apply what they learn in one scenario and under a specific set of conditions to the needs of the patient under a different set of conditions in a new scenario. All without necessitating interpretation or involvement from a human.

For sure, there are many open questions and engineering challenges to be solved before this becomes really possible. For example, demands on computing power and where on the hardware in the patient or cloud any machine learning will take place have to be considered. This is particularly serious in this case because what happens if the BMI needs an internet connection to function properly, but the patient finds themselves in an internet dead zone? Other considerations include the need for further optimized algorithm development, and the need for specialized hardware designed to work specifically with advanced algorithms. And the list goes on.

Yet, despite the challenges, real progress is being made. In one study researchers demonstrated a proof of concept wireless BMI system that took advantage of state of the art flexible electronics and convolution neural networks, one of the most successful approaches to machine learning, in order to allow implanted patients to control a wheelchair.

And in another study, researchers used reinforcement learning, another type of machine learning, to optimize the calibration of a BMI while at the same time transferring what the BMI learned in one scenario to exploring new knowledge (a form of learning referred to as transfer learning - because information is transferred to a new situation). There are even textbooks now aimed at machine learning and artificial intelligence applications to BMIs.

In the end, one day, future patients that need BMIs, as well as their families and loved ones, will be the ultimate beneficiaries of these technologies and the confluence of efforts by thousands of scientists, engineers, and doctors. And that is a hope worth collectively pursuing.

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How Machine Learning Will Enable Technologies That Anticipate What The Brain Thinks - Forbes

International Joint Conferences on Artificial Intelligence is a non-profit corporation founded in California, in 1969 for scientific and educational purposes, including dissemination of information on Artificial Intelligence at conferences in which cutting-edge scientific results are presented and through dissemination of materials presented at these meetings in form of Proceedings, books, video recordings, and other educational materials. IJCAI consists of two divisions: the Conference Division and the AI Journal Division. IJCAI conferences present premier international gatherings of AI researchers and practitioners and they were held biennially in odd-numbered years since 1969.

Starting with 2016, IJCAI conferences are held annually. IJCAI-21 will be held in Montreal, Canada, IJCAI-ECAI-22 in Bologna, Italy, IJCAI-23 in Cape Town, South Africa and IJCAI-PRICAI-24 in Shanghai, P.R. China.

IJCAI is governed by the Board of Trustees, with IJCAI Secretariat in charge of its operations.

IJCAI-PRICAI-20 was held from January 7th until January 15th, 2021 in a virtual Japanese reality. The Conference Committee and the Local Arrangements Committee thank you all for participating.

We are receiving nominations for IJCAI-21 Awards (deadline for nominations February 28, 2021)AI Hub launchedFunding Opportunities for Promoting AI Research Free Access to the AI journal

IJCAI Anti-Discrimination Policy (pdf)IJCAI Privacy Policy (pdf)

IJCAI Organization would like to acknowledge and thank the following platinum level sponsors of its past three conferences in a row:

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Welcome to IJCAI | IJCAI

Many schools and businesses are still struggling to get people to feel safe enough to come back to inside their buildings. Yet returning to in person gatheringsis an important driver for a successfuleconomicpost-COVID rebound.

Smartscreen LLC is hosting a free webinar on utilizing artificial intelligence and decision intelligence to open your buildings, schools and facilities safely and effectively.

The webinar will be hosted on Friday, April 9th at 12noon. You can register by clicking here.

Featured on the webinar will be Dr. Lorien Pratt, Ph.D., the Chief Scientist at Quantellia. Dr. Pratt is amachine learning pioneer,

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Pratt has delivered applied machine learning solutions since 1988. She wrote Link: How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World, co-edited Learning to Learn, and leads machine learning and decision intelligence innovation at Quantellia.

For more information on Smartscreen visit their website at http://www.smartscreenllc.com.

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Learn How Artificial Intelligence and Decision Intelligence is your Key to Safe Re-Openings - TAPinto.net

DUBLIN, April 9, 2021 /PRNewswire/ -- The "Artificial Intelligence and Machine Learning 2020, Volume 1" report has been added to ResearchAndMarkets.com's offering.

This survey gives a comprehensive view of the attitudes, adoption patterns and intentions of artificial intelligence and machine learning developers worldwide. This series focuses on tools, methodologies, and concerns related to implementing machine learning, deep learning, image recognition, pattern recognition and other forms of artificial intelligence as well as efficiently storing, handling, and analyzing large datasets and databases from a wide range of sources.

Artificial intelligence is permeating software development in many ways and many industries, which necessitates a thorough knowledge of how developers are doing this.

This volume includes research and analysis covering topics such as developer demographics and firmographics, artificial intelligence landscape, methods and approaches, resources and services, conversational systems, speech and image recognition, enterprise AI, security, platform adoption, API frameworks, tools and languages, technology adoption, hardware, hardware optimization, parallelism, and high-performance computing, purchasing and influencers, challenges and barriers to success, AI as it relates to IoT, the Cloud, and containerization and more.

This survey consists of 406 in-depth interviews conducted in English with qualified AI and machine learning developers worldwide. This provides a margin of error of 4.7%.

Key Topics Covered:

For more information about this report visit https://www.researchandmarkets.com/r/vojqly

About ResearchAndMarkets.comResearchAndMarkets.com is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends.

Media Contact:

Research and Markets Laura Wood, Senior Manager [emailprotected]

For E.S.T Office Hours Call +1-917-300-0470 For U.S./CAN Toll Free Call +1-800-526-8630 For GMT Office Hours Call +353-1-416-8900

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Artificial Intelligence and Machine Learning: Demographics & Firmographics - PRNewswire

For Immediate Release: April 09, 2021

Today, the U.S. Food and Drug Administration authorized marketing of the GI Genius, the first device that uses artificial intelligence (AI) based on machine learning to assist clinicians in detecting lesions (such as polyps or suspected tumors) in the colon in real time during a colonoscopy.

Artificial intelligence has the potential to transform health care to better assist health care providers and improve patient care. When AI is combined with traditional screenings or surveillance methods, it could help find problems early on, when they may be easier to treat, said Courtney H. Lias, Ph.D. acting director of the GastroRenal, ObGyn, General Hospital and Urology Devices Office in the FDAs Center for Devices and Radiological Health. Studies show that during colorectal cancer screenings, missed lesions can be a problem even for well-trained clinicians. With the FDAs authorization of this device today, clinicians now have a tool that could help improve their ability to detect gastrointestinal lesions they may have missed otherwise.

According to the National Institutes of Health, colorectal cancer is the third leading cause of death from cancer in the United States. Colorectal cancer usually starts from polyps or other precancerous growths in the rectum or the colon (large intestine). As part of a colorectal cancer screening and surveillance plan, clinicians perform colonoscopies to detect changes or abnormalities in the lining of the colon and rectum. A colonoscopy involves threading an endoscope (thin, flexible tube with a camera at the end), through the rectum and throughout the entire length of the colon, allowing a clinician to see signs of cancer or precancerous lesions.

The GI Genius is composed of hardware and software designed to highlight portions of the colon where the device detects a potential lesion. The software uses artificial intelligence algorithm techniques to identify regions of interest. During a colonoscopy, the GI Genius system generates markers, which look like green squares and are accompanied by a short, low-volume sound, and superimposes them on the video from the endoscope camera when it identifies a potential lesion. These signs signal to the clinician that further assessment may be needed, such as a closer visual inspection, tissue sampling, testing or removal, or ablation of (burning) the lesion. The GI Genius is designed to be compatible with many FDA-cleared standard video endoscopy systems.

The FDA assessed the safety and effectiveness of the GI Genius through a multicenter, prospective, randomized, controlled study in Italy with 700 subjects 40-80 years old who were undergoing a colonoscopy for colorectal cancer screening, surveillance, positive results from a previous fecal immunochemical (fecal occult blood) test for blood in the stool or gastrointestinal symptoms of possible colorectal cancer. The primary analyses from the study were based on a sub-population of 263 patients who were being screened or surveilled every 3 years or more. Study subjects underwent either white light standard colonoscopy with the GI Genius (136 patients) or standard white light colonoscopy alone (127 patients).

The primary endpoint of the study compared how often colonoscopy plus GI Genius identified a patient with at least one lab-confirmed adenoma (precancerous tumor) or carcinoma (cancerous tumor) to how often standard colonoscopy made the same identifications. In the study, colonoscopy plus GI Genius was able to identify lab-confirmed adenomas or carcinomas in 55.1% of patients compared to identifying them in 42.0% of patients with standard colonoscopy, an observed difference of 13%.

While use of this device led to more biopsies being performed, there were no adverse events reported with the additional biopsies, such as perforations, infections or bleeding. However, there was a slight increase in the number of lesions biopsied that were not adenomas.

The GI Genius is not intended to characterize or classify a lesion, nor to replace lab sampling as a means of diagnosis. The device does not provide any diagnostic assessments of colorectal polyp pathology, nor does it suggest to the clinician how to manage suspicious polyps. GI Genius only identifies regions of the colon within the endoscopes field of view where a colorectal polyp might be located, allowing for a more extended examination in real time during colonoscopy. It is up to the clinician to decide whether the identified region actually contains a suspected lesion, and how the lesion should be managed and processed per standard clinical practice and guidelines.

The FDA reviewed the GI Genius through the De Novo premarket review pathway, a regulatory pathway for some low- to moderate-risk devices that are novel and for which there is no legally marketed predicate device to which the device can claim substantial equivalence.

The FDA Center for Devices and Radiological Healths Digital Health Center of Excellence is looking to the future of AI-based technology, including in its Artificial Intelligence and Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan.

The FDA granted marketing authorization of the GI Genius to Cosmo Artificial Intelligence, Ltd.

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The FDA, an agency within the U.S. Department of Health and Human Services, protects the public health by assuring the safety, effectiveness, and security of human and veterinary drugs, vaccines and other biological products for human use, and medical devices. The agency also is responsible for the safety and security of our nations food supply, cosmetics, dietary supplements, products that give off electronic radiation, and for regulating tobacco products.

Excerpt from:

FDA Authorizes Marketing of First Device that Uses Artificial Intelligence to Help Detect Potential Signs of Colon Cancer - FDA.gov

The ongoing artificial intelligence (AI) inventorship case of Thaler v. Iancu, et al. (No. 1:20-cv-00903) took another turn on April 6th when U.S. District Court Judge Leonie Brinkema of the Eastern District of Virginia signaled she may rule that AI systems cannot be listed as inventors on U.S. patent applications. Plaintiff Dr. Stephen Thaler is the inventor of an AI system named DABUS, which went on to allegedly invent the subject-matter of two patent applications filed at the United States Patent and Trademark Office (USPTO). Following the USPTOs rejection of the applications on the grounds that the applications were deficient and an AI system cannot be listed as an inventor, Thaler filed a lawsuit against the USPTO in 2020. It should be noted that both the USPTO and Thaler agree that Thaler himself apparently could not be listed as the inventor of the subject-matter of the patent applications.

At a summary judgment hearing in the case, Judge Brinkema stated that Plaintiff Thaler has a huge uphill battle . . . because the statutory language [of the Patent Act] is so crystal clear that an inventor must be living individual, and not a machine. Judge Brinkema further stated that it is the job of legislatures, not the courts, to address such issues as technology rapidly advances; Courts are not legislatures . . . and I think ultimately what youre asking this court to do is legislate.

The potential effects a ruling in favor of Thaler would have on other areas of patent law also arose at the hearing in the context of patent assignments. Specifically, Judge Brinkema questioned how an AI system could assign rights in an invention to which it is named when the assigning party must have intent to assign the rights.

While it remains to be seen how Judge Brinkema will rule, it is likely that the ball will be kicked over to Congress to determine how to handle inventorship by AI systems. Given the tremendous leaps forward that AI has made in the past decade alone, this issue is likely to continue until changes are made to the Patent Act.

[View source.]

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Judge signals that artificial intelligence cannot be named as an inventor in the United States - JD Supra

The BMW Group is publishing an anonymisation solution based on artificial intelligence (AI)that can anonymise objects in photos and videos. Building on the BMW labelling tool Lite, these algorithms (github.com/BMW-InnovationLab) enable targeted protection of relevant information: The user-friendly software tool uses AI to block out or blur objects or people. The granularity and degree of anonymisation can be intuitively adjusted.

AI applications supports us with quality assurance, such as inspection of parts and components, as well as development of our autonomous, smart logistics robots. The AI anonymisation algorithms now published also ensure optimal data privacy and information protection, explains Markus Grneisl, head of Production System, Digitalisation at the BMW Group. Making the anonymisation solution intuitive to operate was an important aspect of development for us, to ensure it can easily be used for a wide range of applications.

The BMW Group uses artificial intelligence for object detection in production, since it offers a particularly high level of robustness even under highly variable boundary conditions. AI-based image processing contributes in this way to maintaining premium quality. The recently released anonymisation solution also relies on artificial intelligence. AI automatically classifies image areas according to their features, so any areas that need to be made unrecognisable can be blocked out for example, when processing photos from production. Different modes of anonymisation can be selected: Respective areas in photos or videos can be blurred, blacked out or pixelated.

The main technology used is the BMW labelling tool Lite, which allows users to label photos and train the AI with just one click. Each label serves as a digital tag that describes the information contained in the photo.

With no-code AI, production staff can create their own artificial intelligence solutions to support them in their individual processes. The new modular anonymisation algorithms allow photos to be processed automatically. In the BMW production system, for instance, areas containing people are deliberately made unrecognisable. Thanks to this AI-based anonymisation solution, there are no limitations on the use of image processing systems.

The published algorithms are freely available to software developers around the world so they can use the algorithms and view, modify and further develop the source code. The BMW Group will also benefit from these further developments. A special feature of this now freely available software package is its simple and uncomplicated application based on the plug-and-play principle. The user does not require any programming skills, specific hardware or additional software.

The BMW Group published selected AI algorithms used in the production system for the first time in 2019. The tremendous amount of feedback we received on the algorithms we published overwhelmed us. Our BMW AI community is very happy with the appreciation we are receiving worldwide. We are seeing many further developments based on our source code and no-code AI approach. We will continue following this approach and sharing our no-code AI algorithms so artificial intelligence can be made accessible to a wide range of users, explains Kai Demtrder, head of Data Transformation and Artificial Intelligence.

The BMW Group uses a variety of applications from the field of artificial intelligence (AI) in production and logistics. AI technology reduces the strain on employees, by relieving them of particularly monotonous or tiring control tasks.

AI applications are used in the BMW Group production system to recognise and classify objects in images. For example, this ensures all vehicles are built in the customised configuration as ordered and that all components are in flawless condition. Another area of application for the anonymisation algorithms is the development of autonomous smart logistics robots STR. The anonymisation algorithms simplify development significantly by using real images to train the robots directly. To make AI accessible to a wide range of users, programmers from the TechOffice in Munich are developing so-called no-code AI, so every user can train an AI model without having to programme a single line of software code. Numerous applications have already been realised in the BMW production system with the help of this AI self-service option.

Further information can be found at:https://github.com/BMW-InnovationLab

SOURCE: BMW Group

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BMW Group scaling artificial intelligence for data privacy in production with innovative anonymisation algorithms - Automotive World

A recent study tested a new digital therapeutic for effectiveness on both substance abuse and depression.

In 2018 more than 20 million Americans met criteria for substance use disorder, particularly alcohol abuse (73%). During the COVID-19 pandemic, alcohol sales have risen sharply, up 54% from the previous year.

In this Mental Health Minute, Judith J. Prochaska, PhD, MPH points out that the pandemic has made it difficult for patients to seek traditional forms of counseling. She discusses a digital therapeutic that can be an adjunct for traditional therapies, and results from the latest study on the treatments efficacy for substance abuse and mood disorders.

Dr Prochaska is a professor in the department of medicine at Stanford University with the Stanford Prevention Research Center and a member of the Stanford Cancer Institute.

Would you recommend a digital therapeutic to your patients? Share comments with your colleagues by emailing PTEditor@mmhgroup.com. Comments may be shared online pending review and editing for style.

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Could Artificial Intelligence Improve Cognitive Behavioral Therapy? - Psychiatric Times

WASHINGTON Artificial intelligence can provide vital savings for the Pentagon in the face of flat or decreasing budgets, the director of the departments top AI office said Friday.

Lt. Gen. Michael Groen, leader of the Joint Artificial Intelligence Center, promoted the efficiencies of AI, particularly for business systems, on the same day the Biden administration announced a topline defense budget proposal of $715 billion, which amounts to an overall decrease, when adjusted for inflation.

In an era of tightening budgets and focus on squeezing out things that are legacy and are not important in the budget, the productivity gains and the efficiency gains that AI can bring to the department, especially through the business process transformation, actually becomes an economic necessity, Groen said at a press briefing. So in a squeezed play between modernizing our warfare that moves at machine speed and tighter budgets, AI is doubly necessary.

The JAIC, tasked with increasing the use of artificial intelligence across the department, wants to drive the Pentagon to operate more like a data-driven software company. That includes establishing data feeds and algorithms that are shared across the department.

Groen said that implementing those practices would create productivity gains, new insights and improved management across the department.

It really represents a transformation of our operating model, Groen said. That operating model will have to create a common data environment where data is shared, data is authoritative, [and] data is available.

He added, its about making our organization, the Department of Defense in this case, as productive and efficient as any of these modern successful data-driven enterprises.

Earlier this month, Groen warned that the departments biggest strategic threat was its own technological obsolescence and called for the department to invest more in emerging technologies, such as artificial intelligence and resilient networks, that will define the future of warfare. Artificial intelligence and associated technologies underpin the DoDs plans to stay competitive, and its the JAICs responsibly to provide best practices and services to assist organizations on their AI efforts.

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Providing those services is core to the organizations pivot to JAIC 2.0, in which it focuses more on providing broad AI-enabling services to department components rather than developing AI products.

Under the JAIC 2.0, we measure our success in the success of others, Groen said.

We come in as archivists of best practice across the department, and say, Hey, show us how youre doing that. Let us learn from you, he said. And then we can share, Hey, you know, theres another agency in the department that has a problem very similar to yours and heres how theyre addressing that. So we played broker for information and expertise across agencies, across services across combatant commands.

That shift has recently manifested itself through the release of solicitations to industry for test and evaluation help and data readiness services in an effort to boost its ability to help department components implement artificial intelligence.

The JAIC is also viewed as central to AI efforts at the Pentagon by the National Security Commission on Artificial Intelligence, a congressionally mandated organization that provided recommendations to boost the countrys AI readiness. The report urges the department to be AI ready by 2025 and suggests the JAIC take on a broad range of responsibilities, from developing workforce initiatives to advising components on AI development.

The JAICs prominence has also increased because of a provision in the most recent National Defense Authorization Act that elevated the centers reporting responsibility to the deputy secretary of defense, a move Groen said gives the JAIC more insight into department priorities and boosts the stature of the center internally.

The move allows the deputy secretary and the rest of the department leadership access to the tools and processes to reinforce their priorities, underline our ethical foundations, integrate our enterprises and transform our business processes, he said.

Bob Work, former deputy secretary of defense and vice chair of the NSCAI, echoed Groens comments at the press conference, arguing that AI leadership is core to the departments future competitiveness.

You have to have top down leadership, you cannot say AI is important and let all of the agencies and subordinate departments figure out what that means, Work said.

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JAIC director: With flat budgets, turn to AI to save money - C4ISRNet - C4ISRNet