Category Archives: Artificial Intelligence

In a diplomatic win for Beijing on Monday, the United Nations General Assembly unanimously adopted a China-led resolution that urges the international community to create a free, open, inclusive and non-discriminatory business environment among wealthy and developing nations for artificial intelligence development.

More than 140 nations, including the United States, co-sponsored the non-binding resolution affirming that all nations should enjoy equal opportunities in the non-military domain, calling for global cooperation to assist developing countries facing unique challenges and ensure they will not be further left behind.

Fu Cong, Chinas permanent representative to the United Nations, said after the assembly session that a fragmented approach toward AI, toward the digital technology, is not going to benefit anybody.

He added that the resolution was proposed to emphasize the important role that the UN could play on AI governance as the most inclusive organization.

Describing the significance of the measure as great and far-reaching, the envoy noted that AI technology was advancing quickly and the gap between the North and South, especially between the developing countries and the developed countries, is also widening.

Ambassador Fu, who served as the director-general of the department of arms control at the Chinese foreign affairs ministry from 2018 to 2022, also said that China was very thankful, and were very appreciative of the positive role that the US has played in this whole process.

He added that the issue of AI had been discussed at a very senior level, at the foreign-ministers level, and also even at the head-of-state level.

So we do look forward to intensifying our cooperation with the United States, and, for that matter, with all countries in the world on this issue, he said.

Beijings initiative also follows the assemblys adoption of the first global resolution on AI in March. Proposed by Washington and co-sponsored by China and over 120 nations, the measure encouraged countries to safeguard human rights, protect personal data and monitor AI for potential risks.

A senior official from US President Joe Bidens administration later said that consensus had been achieved after intense discussions among countries with differing views.

On Monday, Ambassador Fu called the two resolutions complementary, saying the earlier one was more general and the Chinese one was more focused on the capacity building.

Beijing has sought to incorporate voices from the developing world into discussions on managing AI. In October, China released its Global AI Governance Initiative, saying that all countries, regardless of their size, strength, or social system, should have equal rights to develop and use AI.

Beijing is seen as trying to ensure that the US solely does not dominate the discourse on setting global standards for AI.

The US and China also remain locked in a competition to advance in the hi-tech fields of AI and semiconductors.

In March, Washington revised regulations further limiting Chinas access to US-made AI chips and chip-making tools. The export controls were initially introduced in October 2022 to prevent Beijing from leveraging American technology for military modernization. They were updated a year later to eliminate loopholes.

In another push to hobble Beijings ability to gain cutting-edge technologies like semiconductors, quantum computing and AI, Biden signed an executive order in August 2023 banning US individuals and companies from investing in sensitive sectors in China.

05:03

How does Chinas AI stack up against ChatGPT?

How does Chinas AI stack up against ChatGPT?

The US Treasury Department, which is defining the restrictions in the measure, said last week that they would focus on the next generation of military, intelligence, surveillance or cyber-enabled capabilities that pose national security risks to the United States.

On Monday, Ambassador Fu called on the US to lift the sanctions in line with the newly adopted resolution.

If people are true to the content of this resolution, it says that it is important to foster inclusive business environment. We dont think that the US actions [are] along that line, he said.

Read the rest here:

China-led resolution on artificial intelligence passes in United Nations - South China Morning Post

DENVER, June 27, 2024 /PRNewswire/ --Voyager Space (Voyager), a global leader in space exploration, today announced a strategic partnership with Palantir Technologies, Inc. (NYSE:PLTR), a leading builder of artificial intelligence (AI) systems for the modern enterprise. Together, Voyager andPalantir will rapidly advance the space and defense technology sectors by integrating Palantir's cutting-edge AI tools across the Voyager enterprise.

This partnership solidifies Voyager's commitment to leading the space industry in AI-driven innovation, ensuring robust and agile solutions for defense and commercial applications. Expanding on a previous Memorandum of Understanding (MOU) announced earlier this year, Voyager will now fully integrate Palantir's AI capabilities into their defense solutions, benefiting from Palantir's deep expertise delivering for the Department of Defense (DoD). This collaboration enhances communications, military research and development, as well as intelligence and space research, making space more accessible to the defense community and vice versa.

"We are thrilled to partner with Palantir, a company that shares our vision for leveraging technology to drive transformative change," said Matt Kuta, President, Voyager Space. "By embracing Palantir's game-changing AI across our operations, we not only enhance Voyager's defense-tech capabilities, but also set a new standard for the broader aerospace industry. This collaboration will enable us to deliver unprecedented value and innovation to our customers and stakeholders."

Voyager will leverage Palantir Foundry and the Artificial Intelligence Platform (AIP) to drive value in its in-house payload management system for International Space Station customers today, as well as onboard the Starlab commercial space station in the future. It is also building aprototype "Customer Hub" for its customers to submit payload requests via Palantir's software.

The partnership also bolsters Voyager's defense segment,offering the opportunity to useAI to process and optimize flight and testing data on solid fuel thrusters to ensure smooth flight. Palantir's softwarecan alsohelp power increased real time signal data processing and more precise targeting for Voyager's optical communications systems for DoD customers.

"We look forward to deepening our collaboration with Voyager Space," said Shyam Sankar, CTO of Palantir."Palantir is committed to building transformative AI solutions across every domain. Our work with Voyager enables us to continue expanding the boundaries of these capabilities to better meet the context of our customer's mission. Together, we will drive the innovation our nation needs to create resilient infrastructure, scale production, and uphold national security."

Today's news builds on the recently announced partnership between Starlab Space and Palantir. As Voyager continues to embrace the benefits of AI alongside Palantir, both companies are poised to lead the defense and space industry into a new era of innovation.

About Voyager SpaceVoyager Space is dedicated to building a better future for humanity in space and on Earth. With over 35 years of spaceflight heritage and over 2,000 successful missions, Voyager is powering the commercial space revolution. Voyager delivers exploration, technology, and defense solutions to a global customer base that includes civil and national security agencies, commercial companies, academic and research institutions, and more.

About Palantir Technologies Inc. Palantir builds category-leading software that empowers organizations to create and govern artificial intelligence across public and private networks. Since 2003, we have helped some of the world's most important organizations solve their most difficult problems. Foundational Software of Tomorrow. Delivered Today. Additional information is available athttps://www.palantir.com.

Cautionary Statement Concerning Forward-Looking StatementsThis press release contains "forward-looking statements." All statements, other than statements of historical fact, including those with respect to Voyager Space, Inc.'s (the "Company's") mission statement and growth strategy, are "forward-looking statements." Although the Company's management believes that such forward-looking statements are reasonable, it cannot guarantee that such expectations are, or will be, correct. These forward-looking statements involve many risks and uncertainties, which could cause the Company's future results to differ materially from those anticipated. Potential risks and uncertainties include, among others, general economic conditions and conditions affecting the industries in which the Company operates; the uncertainty of regulatory requirements and approvals; and the ability to obtain necessary financing on acceptable terms or at all. Readers should not place any undue reliance on forward-looking statements since they involve these known and unknown uncertainties and other factors which are, in some cases, beyond the Company's control and which could, and likely will, materially affect actual results, levels of activity, performance or achievements. Any forward-looking statement reflects the Company's current views with respect to future events and is subject to these and other risks, uncertainties and assumptions relating to operations, results of operations, growth strategy and liquidity. The Company assumes no obligation to publicly update or revise these forward-looking statements for any reason, or to update the reasons actual results could differ materially from those anticipated in these forward-looking statements, even if new information becomes available in the future.

SOURCE Voyager Space

See the original post here:

Voyager Space and Palantir Announce Strategic Partnership Leveraging Artificial Intelligence to Drive Innovation in ... - PR Newswire

On June 25, Moschino made official its partnership with Yoobic, an artificial intelligence-powered platform specializing in employee experience for frontline teams. The Italian luxury brand, owned by the Aeffe group, hopes in particular to accelerate the skills development of its sales advisors and improve communication between its different markets.

Its not easy to get boutique teams on board with e-learning training. Most of the programs available target employees who are able to telecommute, while few solutions exist for retail teams. Breaking the status quo, Yoobic, an American tech company founded in 2014, deployed its microlearning platform at Moschino last year. The American HR tech company has thus reported initial results showing the effectiveness of its hybrid model oscillating between social media application and game.

Training can be fun, exciting and effective. This is how Yoobic describes its micro-learning platform for in-store teams.

My aim was to find a training tool that was smarter, more user-friendly and, above all, available on all smartphones, said Luca Trignano, Global Retail Training Manager at Moschino, who was delighted with the initial results of the solutions deployment, to the point of becoming a fundamental tool for Moschino store teams.

The Italian House reported certain difficulties in creating and launching a sales team training program that had not been overcome by previous solutions used.

By turning to Yoobic, the Italian luxury ready-to-wear and accessories brand discovered training modules adapted to retail constraints (standing, high customer presence, time-consuming management of point-of-sale and administrative tasks, increased use of cell phones for clienteling, speed of execution and high availability).

The first phases of the Moschino roll-out are already showing results. The completion rate for online courses has climbed to 98%, and store teams have expressed their enthusiasm for immersing themselves in the training modules. So far, the average course ratings are 4.7 out of five stars, says the press release.

In order to develop engaging and stimulating training, including for customers, Moschino has made available to its sales teams a mobile application in collaboration with Yoobic. The app features an arsenal of learning and development content, including short videos, images and quizzes.

Drawing on the main principles of HR gamification, inspired by the codes of video games (passing and unlocking levels, obtaining badges and rewards, sharing and comparing results), the solution developed by Yoobic also enables Moschinos senior managers to generate healthy competition between colleagues. It awards points on the leaderboard for completion of the course and participation in the competition.

Read also > VIVATECH: FANCYTECH, WINNER OF THE LVMH INNOVATION AWARD 2024

Featured Photo: Moschino

See the original post:

Moschino uses artificial intelligence to train its retail teams - Luxus Plus

ST. CLOUD (WJON News) --A team from a local university has been accepted into a prestigious Artificial Intelligence (AI) program. A group of 7 faculty, staff, and administrators from St. Cloud State Univerisity has been accepted into the American Association of Colleges and University (AAC&U) Institute on AI, Pedagogy, and the Curriculum.

The program will let St. Cloud State respond to opportunities and challenges AI creates for classes. The team will take part in virtual events, mentorship sessions, and monthly meetings in 2024-2025 that will focus on developing strategies for increasing AI literacy among students.

Online and Distance Learning Director Dave Blanchard says the goal is to help SCSU shift to a space where there are opportunities for students to leverage AI within their studies. The University says the initiative will significantly impact St. Cloud State in effectively equipping faculty with the knowledge and tools to incorporate AI into their teaching. The team includes:

-Dr. Melissa Hanzsek-Brill, Dean of the College of Education and Learning Design -Dr. Mary Clifford, Professor of Criminal Justice -Dr. Mark Petzold, Professor of Computer Science -Dr. Ruthanne Kim, Professor and Faculty Lead for Community Antiracism Education -Dr. Janet Tilstra, Professor and Director of the Center for Excellence in Teaching and Learning -Dave Blanchard, Director of Online and Distance Learning -Mark Gill, Director of SCSU Visualization Lab

READ MORE FROM AUTHOR PAUL HABSTRITT:

Originally posted here:

SCSU Team Accepted Into Prestigious Artificial Intelligence Prog. - WJON News

AI has made life and work complicated for all sorts of professional artists and other workers.

Unfortunately, some people see this as an opportunity to be unkind.

But as youll see in this story, this can be satisfying later on for the injured party.

I do art commissions on the side.

My brother jokes that I need a new side hustle now that AI art tools are around and no one really needs artists anymore.

Im not a full time artist, but those jokes annoyed me, so I tell him to knock it off.

His brother wont give it a rest.

He doesnt and says that I should get used to it.

On Monday, he got called into a meeting where he was told hes getting laid off as the company hes working for has finished automating a lot of processes.

Because of that they dont have enough work for all the technicians. Since hes the newest tech, he got cut.

He told the family in a group chat and we did the So sorry to hear that and do you need any help? cycle for half an hour.

His brothers remarks have come back to haunt him.

In the chat, I promised to help him tidy up his resume.

After that, I texted to him, Guess AI was coming for your job too lol.

He stopped responding and his girlfriend texted me saying that I shouldnt have kicked him while he was down.

AITA?

Heres what people are saying.

A lot of people made this point. This is not a healthy relationship.

I agree. This is too sore of a spot.

Exactly. Its apples to oranges.

That would be a healthy approach, but I doubt they would do that.

I sort of agree, but the timing was just bad.

At least he didnt make the joke in the group chat

If you enjoyed that story, check out what happened when a guy gave ChatGPT $100 to make as money as possible, and it turned out exactly how you would expect.

More here:

His Brother Said Artificial Intelligence Would Eliminate His Side Hustle And Then Got Laid Off, So He Responded That ... - Twisted Sifter

Artificial intelligence-powered video maker Runway has officially launched its new Gen-3 Alpha model after teasing its debut a few weeks ago. The Gen-3 Alpha video creator offers major upgrades in creating hyper-realistic videos from user prompts. It's a significant advancement over the Gen-2 model released early last year.

Runway's Gen-3 Alpha is aimed at a range of content creators, including marketing and advertising groups. The startup claims to outdo any competition when it comes to handling complex transitions, as well as key-framing and human characters with expressive faces. The model was trained on a large video and image dataset annotated with descriptive captions, enabling it to generate highly realistic video clips. As of this writing, the company is not revealing the sources of its video and image datasets.

The new model is accessible to all users signed up on the RunwayML platform, but unlike Gen-1 and Gen-2, Gen-3 Alpha is not free. Users must upgrade to a paid plan, with prices starting at $12 per month per editor. This move suggests Runway is ready to professionalize its products after having the chance to refine them, thanks to all of the people playing with the free models.

Initially, Gen-3 Alpha will power Runway's text-to-video mode, allowing users to create videos using natural language prompts. In the coming days, the model's capabilities will expand to include image-to-video and video-to-video modes. Additionally, Gen-3 Alpha will integrate with Runway's control features, such as Motion Brush, Advanced Camera Controls, and Director Mode.

Runway stated that Gen-3 Alpha is only the first in a new line of models built for large-scale multimodal training. The end goal is what the company calls "General World Models," which will be capable of representing and simulating a wide range of real-world situations and interactions.

The immediate question is whether Runway's advancements can meet or exceed what OpenAI is doing with its attention-grabbing Sora model. While Sora promises one-minute-long videos, Runway's Gen-3 Alpha currently supports video clips that are only up to 10 seconds long. Despite this limitation, Runway is betting on Gen-3 Alpha's speed and quality to set it apart from Sora, at least until it can augment the model as they have planned, making it capable of producing longer videos.

The race isn't just about Sora. Stability AI, Pika, Luma Labs, and others are all eager to claim the title of best AI video creator. As the competition heats up, Runway's release of Gen-3 Alpha is a strategic move to assert a leading position in the market.

Sign up for breaking news, reviews, opinion, top tech deals, and more.

Original post:

Forget Sora, Runway is the AI video maker coming to blow your mind - TechRadar

Investing

Published: July 1, 2024 2:20 pm

Picking the individual winners of the burgeoning artificial intelligence (AI) race is no simple task. But one exchange-traded fund (ETF) that provides investors with exposure to a basket of AI stocks could be the solution.

While many investors struck gold by purchasing shares of NVDIA (NASDAQ: NVDA) before the chipmakers stock took off last year, the AI-adjacent company is more of the exception than the rule.

Pure play AI companies, on the other hand, have had less predictable successes, with some like C3.ai (NYSE: AI) seeing precipitous rises and falls. C3.ai, which produces AI applications for other enterprises, saw its stock surge to $161 per share by late 2020. At the time of writing, shares of the company are now trading for $28.96.

Forecasts suggest that the global AI market could increase exponentially by the early part of the next decade. By some analysts estimates, that growth could be as much as 300 times its 2022 valuation of $39 billion, which would translate to an astounding $1.3 trillion by 2032.

But how do investors identify the likely winners? Rather than picking one or two companies operating in the AI space and simply wishing for the best, ETFs with holdings spread across all facets of the AI industry allow investors to gain exposure to the trend without overexposing themselves to any individual holding.

In this way, not only are these ETFs providing broad exposure to AI with companies offering varying levels of involvement to the technology, but in doing so, these funds are simultaneously reducing overall risk exposure.

And just as ETFs go, the options for ones leveraged to the AI industry are bountiful. However, just like the stocks they hold, not all ETFs are created equally.

There are no fewer than 38 AI-themed ETFs currently trading on the major exchanges in the U.S. Some offer equal weighting, some prefer heavier allocations to the Magnificent Seven stocks. Some are actively managed with portfolio positions constantly shuffled.

They vary considerably by size, too, with some having assets under management (AUM) as low as $532,360 and others reaching as high as $2.72 billion.

But when it comes to finding a fund with the best combination of high growth potential, Big Tech names, diverse AI industry exposure, significant AUM coupled with a modest expense ratio, one ETF in particular takes the cake.

Enter the Global X Artificial Intelligence & Technology ETF (NASDAQ: AIQ), which has posted an eye-catching 138% gain since its inception in May 2018 and has gained over 17% so far in 2024. According to Global Xs website, the ETF has net assets of $2.08 billion and a total expense ratio of 0.68%.

And while its size and per share appreciation have been impressive so far, it is the funds holdings that should garner a lot of attention. By industry, AIQ spans packaged software, semiconductors, internet software and services, information technology services, telecommunications equipment, internet retail, and industrial conglomerates.

That breadth is expansive, but looking at the names among its top weighted holdings provides more insight into why this ETF is an AI powerhouse:

Of course, those are not all of AIQs holdings, but they are the big names with some of the heaviest weightings. And looking at that list, you can see why the AI ETF was capable of producing such enormous gains for shareholders since it debuted in 2018.

As AI expands out of its earliest phase, when it was constricted to pure play stocks, cloud services, and data centers, the technology is now finding its way into streaming services (Netflix), e-commerce (Alibaba), customer relationship management (Salesforce), and numerous other facets of the economy.

Rather than hoping any one of the aforementioned companies emerges as the biggest winner of the next phase of AI implementation, investing in a fund like the Global X Artificial Intelligence & Technology ETF can provide investors with the best of broad exposure and reduced risk.

Thank you for reading! Have some feedback for us? Contact the 24/7 Wall St. editorial team.

Read more:

This Is My Top Artificial Intelligence (AI) ETF to Buy Right Now - 24/7 Wall St.

Every day, thousands of marines perform routine data-collection tasks and make hundreds of data-based decisions. They compile manning data on whiteboards to decide to staff units, screenshot weather forecasts and paste them into weekly commanders update briefings, and submit training entries by hand. But anyone who has used ChatGPT or other large-scale data analytic services in the last two years knows the immense power of generative AI to streamline these processes and improve the quality of these decisions by basing them on fresh and comprehensive data.

The U.S. Marine Corps has finally caught wind. Gen. Eric Smiths new message calls for the service to recognize that [t]echnology has exponentially increased informations effects on the modern battlefield, making our need to exploit data more important than ever. The services stand-in forces operating concept relies on marine operating forces to integrate into networks of sensors, using automation and machine learning to simplify decision processes and kill chains. Forces deployed forward in littoral environments will be sustained by a supply system that uses data analysis for predictive maintenance, identifying which repair parts the force will need in advance.

However, there is a long way to go before these projections become reality. A series of interviews with key personnel in the Marine Corps operating forces and supporting establishment, other services, and combatant commands over the past six months reveal that the service needs to move more quickly if it intends to use AI and machine learning to execute this operating concept. Despite efforts from senior leaders to nudge the service towards integrating AI and machine learning, only incremental progress has been made.

The service depends on marines possessing the technical skills to make data legible to automated analytic systems and enable data-informed decisions. Designating a Marine expeditionary force or one of its major subordinate commands as the lead for data analysis and literacy would unify the services two-track approach by creating an ecosystem that will allow bottom-up creativity, scale innovation across the force, and speed the integration of these technologies into the fleet and supporting establishment.

New Technologys Potential to Transform Operations, Logistics, and Education

AI, machine learning, and data analysis can potentially transform military education, planning, and operations. Experiments at Marine Corps University have shown that they could allow students to hone operational art in educational settings by probing new dimensions of complicated problems and understanding the adversarys system. AI models, trained on enemy doctrinal publications and open-source information about troop employment, can use probabilistic reasoning to predict an enemys response. This capability could supplement intelligence red teams by independently analyzing the adversarys options, improve a staffs capacity for operational planning, or simply give students valuable analytic experience. And NIPRGPT, a new Air Force project, promises to upend mundane staff work by generating documents and emails in a secure environment.

Beyond education and planning, AI and machine learning can transform how the Marine Corps fights. During an operation, AI could employ a networked collection of manned and unmanned systems to reconnoiter and attack an adversary. It could also synthesize and display data from sensor networks more quickly than human analysts or sift through thousands of images to identify particular scenes or locations of interest. Either algorithms can decide themselves or enable commanders to make data-informed decisions in previously unthinkable ways. From AI-enabled decision-making to enhanced situational awareness, this technology has the potential to revolutionize military operations. A team of think tank researchers even used AI recently to rethink the Unified Command Plan.

But, achieving these futuristic visions will require the service to develop technical skills and familiarity with this technology before implementing it. Developing data literacy is a prerequisite to effectively employ advanced systems, and so this skill is as important as anything else the service expects of marines. Before the Marine Corps can use AI-enabled swarms of drones to take a beachhead or use predictive maintenance to streamline supply operations, its workforce needs to know how to work with data analysis tools and be comfortable applying them in everyday work settings.

Delivering for the Marine Corps Today

If the Marine Corps wants to employ machine learning and AI in combat, it should teach marines how to use them in stable and predictable garrison operations. Doing so could save the service tens of thousands of hours annually while increasing combat effectiveness and readiness by replacing the antiquated processes and systems the fleet marine force relies on.

The operating forces are awash with legible data that can be used for analysis. Every unit has records of serialized equipment, weapons, and classified information. Most of these records are maintained in antiquated computer-based programs of record or Excel spreadsheets, offering clear opportunities for optimization.

Furthermore, all marines in the fleet do yearly training and readiness tasks to demonstrate competence in their assigned functions. Nothing happens to this data once submitted in the Marine Corps Training Information Management System no headquarters echelon traces performance over time to ensure that marines are improving, besides an occasional cursory glance during a Commanding Generals Inspection visit. This system is labor intensive, requiring manual entries for each training event and each individual marines results.

Establishing and analyzing performance standards from these events could identify which units have the most effective training regimens. Leaders who outperform could be rewarded, and a Marine expeditionary force could establish best practices across its subordinate units to improve combat readiness. Automating or streamlining data entry and analysis would be straightforward since AI excels at performing repetitive tasks with clear parameters. Doing so would save time while increasing the combat proficiency of the operating forces.

Marines in the operating forces perform innumerable routine tasks that could be easily automated. For example, marines in staff sections grab data and format it into weekly command and staff briefings each week. Intelligence officers retrieve weather forecast data from their higher headquarters. Supply officers insert information supply levels into the brief. Medical and dental readiness numbers are usually displayed in a green/yellow/red stoplight chart. This data is compiled by hand in PowerPoint slide decks. These simple tasks could be automated, saving thousands of hours across an entire Marine expeditionary force. Commanders would benefit by making decisions based on the most up-to-date information rather than relying on stale data captured hours before.

The Marine Corps uses outdated processes and systems that waste valuable time that could be used on training and readiness. Using automation, machine learning, and AI to streamline routine tasks and allow commanders to make decisions based on up-to-date data will enable the service to achieve efficiency savings while increasing its combat effectiveness. In Smiths words, combining human talent and advanced processes [will allow the Marine Corps] to become even more lethal in support of the joint force and our allies and partners.

The Current Marine Corps Approach

The service is slow in moving towards its goals because it has decided, de facto, to pursue a two-track development strategy. It has concentrated efforts and resources at the highest echelons of the institution while relying on the rare confluence of expertise and individual initiative for progress at the lowest levels. This bifurcated approach lacks coherence and stymies progress.

Marine Corps Order 5231.4 outlines the services approach to AI. Rather than making the operating forces the focus of effort, the order weights efforts in the supporting establishment. The supporting establishment has the expertise, resources, and authority to manage a program across the Marine Corps. But it lacks visibility into the specific issues facing individuals that could be solved with AI, machine learning, or automated data analysis.

At the tactical levels of the service, individuals are integrating these tools into their workflows. However, without broader sponsorship, this mainly occurs as the result of happy coincidence: when a single person has the technical skills to develop an automated data solution, recognizes a shortfall, and takes the initiative to implement it. Because the skills required to create, maintain, or customize projects for a unit are uncommon, scaling adoption or expanding the project is difficult. As a result, most individual projects wither on the vine, and machine learning, AI, and data analysis have only sporadically and temporarily penetrated the operating forces.

This two-track approach separates resources and problems. This means that the highest level of service isnt directly involved in success at the tactical level. Tactical echelons dont have the time, resources, or tasking to develop and systematize these skill sets on their own. Whats needed is a flat and collaborative bottom-up approach with central coordination.

The 18th Airborne Corps

Marine Corps doctrine and culture advocate carefully balancing centralized planning with decentralized execution and bottom-up refinement. Higher echelons pass flexible instructions to their subordinates, increasing specificity at each level. Leaders ensure standardization of training, uniformity of effort, and efficient use of resources. Bottom-up experimentation applies new ideas to concrete problems.

Machine learning and data analysis should be no different. The challenge is finding a way to link individual innovation instances with the resources and influence to scale them across the institution. The Armys use of the 18th Airborne Corps to bridge the gap between service-level programs and individual initiatives offers a clear example for how to do so.

The 18th Airborne Corps fills a contingency-response role like the Marine Corps. Located at Fort Liberty, it is the headquarters element containing the 101st and 82nd Airborne Divisions, along with the 10th Mountain and 3rd Infantry Divisions. As part of a broader modernization program, the 18th Airborne Corps has focused on creating a technology ecosystem to foster innovation. Individual soldiers across the corps can build personal applications that aggregate, analyze, and present information in customizable dashboards that streamline work processes and allow for data-informed decision-making.

For example, soldiers from the 82nd Airborne Division created a single application to monitor and perform logistics tasks. The 18th Airborne Corps Data Warfare Company built a tool for real-time monitoring of in-theater supply levels with alerts for when certain classes of supply run low. Furthermore, the command integrates these projects and other data applications to streamline combat functions. For example, the 18th Airborne Corps practices integrating intelligence analysis, target acquisition, and fires through joint exercises like Scarlet Dragon.

As well as streamlining operational workflows, the data analytics improve training and readiness. The 18th Airborne Corps has developed a Warrior Skills training program in which they collect data to establish a baseline against which it can compare individual soldiers skills over time. Finally, some of the barracks at Fort Liberty have embedded QR codes that soldiers scan to check in when theyre on duty.

These examples demonstrate how a unit of data-literate individuals can leverage modern technology to increase the capacity of the entire organization. Many of these projects could not have been scaled beyond institutional boundaries without corps-level sponsorship. Furthermore, because the 18th Airborne Corps is an operational-level command, it connects soldiers in its divisions with the Armys service-level stakeholders.

Designating a Major Command as Service Lead

If the Marine Corps followed the 18th Airborne Corps model, it would designate one operating force unit as the service lead for data analysis and automation to link service headquarters with tactical units. Institutionalizing security systems, establishing boundaries for experimentation, expanding successful projects across a Marine expeditionary force, and implementing a standardized training program would create an ecosystem to cultivate the technical advances service leaders want.

This proposed force would also streamline the interactions between marines and the service and ensure manning continuity for units that develop data systems to ensure efforts do not peter out as individuals rotate to new assignments. Because of its geographic proximity to Fort Liberty, and as 2d Marine Division artillery units have already participated in the recent Scarlet Dragon exercises and thus have some familiarity with the 18th Airborne Corps projects, II Marine Expeditionary Force is a logical choice to serve as the service lead.

Once designated, II Marine Expeditionary Force should establish an office, directorate, or company responsible for the entire forces data literacy and automation effort. This would follow the 18th Airborne Corps model of establishing a data warfare company to house soldiers with specialized technical skills. This unit could then develop a training program to be implemented across the Marine expeditionary force. The focus of this effort would be a rank-and-billet appropriate education plan that teaches every marine in the Marine expeditionary force how to read, work with, communicate, and analyze data using low- or no-code applications like PowerBI or the Armys Vantage system, with crucial billets learning how to build and maintain these applications. Using the work it is undertaking with Training and Education Command, combined with its members academic and industry expertise, the Marine Innovation Unit (of which I am a member) could develop a training plan based on the Armys model that II Marine Expeditionary Force could use and would work alongside the proposed office to create and implement this training plan.

This training plan will teach every marine the rudimentary skills necessary to implement simple solutions for themselves. The coordinating office will centralize overhead, standardize training, and scale valuable projects across the whole Marine expeditionary force. It would link the high-level service efforts with the small-scale problems facing the operating forces that data literacy and automation could fix.

All the individuals interviewed agreed that engaged and supportive leadership has been an essential precondition for all successful data automation projects. Service-level tasking should ensure that all subordinate commanders take the initiative seriously. Once lower-echelon units see the hours of work spent on rote and mundane tasks that could be automated and then invested back into training and readiness, bureaucratic politics will melt away, and implementation should follow. The key is for a leader to structure the incentives for subordinates to encourage the first generation of adopters.

Forcing deploying units to perform another training requirement could overburden them. However, implementing this training carefully would ensure it is manageable. The Marine expeditionary force and its subordinate units headquarters are not on deployment rotations, so additional training would not detract from their pre-deployment readiness process. Also, implementing these technologies would create significant time savings, freeing up extra time and manpower for training and readiness tasks.

Conclusion

Senior leaders across the Department of Defense and Marine Corps have stated that AI and machine learning are the way forward for the future force. The efficiency loss created by the services current analog processes and static data (let alone the risk to mission and risk to force associated with these antiquated processes in a combat environment) is enough reason to adopt this approach. However, discussions with currently serving practitioners reveal that the Marine Corps needs to move more quickly. It has pursued a two-track model with innovation at the lowest levels and resources at the highest. Bridging the gap between these parallel efforts will be critical to meaningful progress.

If the Marine Corps intends to incorporate AI and machine learning into its deployed operations, it should build the groundwork by training its workforce and building familiarity during garrison operations. Once marines are familiar with and able to employ these tools in a stable and predictable environment, they will naturally use them when deployed to a hostile littoral zone. Designating one major command to act as the service lead would go a long way toward accomplishing that goal. This proposed command would follow the 18th Airborne Corps model of linking the strategic and tactical echelons of the force and implementing new and innovative ways of automating day-to-day tasks and data analysis. Doing so will streamline garrison operations and improve readiness.

Will McGee is an officer in the U.S. Marine Corps Reserves, currently serving with the Marine Innovation Unit. The views in this article are the authors and do not represent those of the Marine Innovation Unit, the U.S. Marine Corps, the Defense Department, or any part of the U.S. government.

Image: Midjourney

Link:

Integrating Artificial Intelligence and Machine Learning in the Marine Corps - War On The Rocks

How do you revive a diminished but once mighty brand in 2024? Two letters: AI.

Toys R Us which, after going bankrupt in the late 2010s, is now mostly a shop-inside-a-shop in Macys department stores released what it called the first-ever brand film using OpenAIs text to video tool, Sora.

Using generative artificial intelligence in advertising is still in its novelty phase. But that doesnt mean its not risky for a brand.

The Toys R Us ad is pretty obviously AI its got that whole surreally smooth, this-is-kinda-creepy-but-I-dont-know-why aesthetic to it.

Even if you dont lay awake at night pondering the Toys R Us origin story, thats not really the point. The point is, Hey, we made this with AI!

This ad will get publicity. Whether it will really compel people to return to Toys R Us I think is an open question, said Christine Moorman, a marketing professor at Duke Universitys Fuqua School of Business.

Moorman said when a brand dips its toes in AI ads, its betting that its target consumer will find it cool and interesting not scary and a threat to their livelihood.

If youre an older consumer, you may have more of that ick factor, that fear factor. If youre younger, you may be more in awe of it, she said.

Its not just Toys R Us. Companies like Under Armour and Levis have also hyped their use of AI in marketing materials, from short commercials to digital fashion models.

Right now, companies are using AI to help brand themselves to consumers and investors as ready for the future, which presents an opportunity for other brands to counterprogram: Hey,thoseguys are with the robots. Were 100% certified organic human.

Dove recently did that. They sort of like came out and said, were not using AI to create various things in our marketing. Were sort of human-focused, said Asa Hiken, a technology reporter at Ad Age.

While AI ads are pretty easy to spot now, with their rough-around-the-edges fingers and faces and transitions, he said, they will get better.

Although he doesnt see the controversy around them going away, he also doesnt think there will ever be a time when everyones on board.

Even with some pretty awesome technology, like cellphones, theres a lot of people that are like, This is causing a lot of harm,' he said.

Although it is hard to find any ad these days thats not optimized for your phone.

Theres a lot happening in the world. Through it all, Marketplace is here for you.

You rely on Marketplace to break down the worlds events and tell you how it affects you in a fact-based, approachable way. We rely on your financial support to keep making that possible.

Your donation today powers the independent journalism that you rely on. For just $5/month, you can help sustain Marketplace so we can keep reporting on the things that matter to you.

Excerpt from:

AI advertising may repel as well as attract - Marketplace

Open this photo in gallery:

Sundar Pichai and Daniel Sank, right, with one of Google's Quantum Computers in the Santa Barbara lab, in California.Handout ./Reuters

Christian Weedbrook is founder of the quantum technologies company Xanadu.

Data centres are the backbone of all of our digital lives. They are used behind the scenes by most companies to store and process information for streaming services, e-commerce, search, social media, and more recently artificial intelligence (AI) tools such as ChatGPT.

In essence, a data centre is a collection of computer server racks housed in a physically secure facility requiring large amounts of electricity and water. The server racks are connected to each other by fibre optic cables, shuttling information between them.

Its a domain we dont often think of, even if we all rely on services that originate from it. And in this domain, a crisis is coming. The growth of what the industry calls compute another way of describing the processing power of the data centres is unsustainable as it demands increasingly vast amounts of energy and resources and exacerbates environmental challenges.

Along with this extreme usage comes significant problems. Data centres currently count for 1.5 per cent of the worlds energy consumption and this is projected to increase. All of this is leading to concerns both here in Canada and abroad that we dont have enough energy for our future data centre ambitions. One of the biggest drivers of this increase in data centre usage is AI; by 2027, it is estimated that AI applications will account for 20 per cent to 25 per cent of all such usage.

A number of solutions are being considered. These include powering data centres using nuclear, sun-chasing initiatives and the more traditional approaches of wind and solar power. But none of these solutions comes close.

Most forms of renewable energy are intermittent and can only be built on specific sites. Nuclear energy has a dangerous reputation, making widespread implementation difficult. And these solutions do nothing to address the underlying efficiency of the computations happening in these data centres, which are bounded by the domain of classical physics.

There is only one real solution: Quantum computing goes beyond this and enables exponential improvements in efficiency, allowing far more to be done per kilowatt-hour (kWh) of energy on certain applications.

A quantum computer is a computer that can perform certain important problems exponentially faster than normal computers by leveraging the properties of quantum physics. Small quantum computers exist today, but none exist at the scale of a data centre (scaling up while keeping their quantum-ness is hard).

Once built, a data centre containing quantum server racks will be half a football field in size, and networked using fibre optics. A single quantum data centre will have energy consumption similar to a single traditional data centre. But one quantum data centre, for key applications, will be equivalent to hundreds or thousands of standard data centres.

But by far the biggest energy savings will come from the innovation and discoveries of the quantum data centres helping to find more efficient ways of doing things. Such discoveries and others like it would perhaps take a century to achieve using traditional tools. A quantum data centre could deliver them much sooner.

It is only these data-centre-sized quantum computers that will be able to solve problems such as developing novel catalysts for the synthesis of synthetic hydrocarbons, new carbon capture and sequestration solutions, discovering new materials to create next-generation batteries.

The idea of quantum computing as the future of data centres has only recently begun to pick up steam. There have been significant investments by the Australian government to build a quantum data centre in Brisbane, and by the U.S. state of Illinois for a cryogenics facility (a key component of a quantum data centre) among other infrastructure in Chicago.

It is imperative that Canada follows suit; otherwise, from an economic-independence, national-security and energy point of view, it will be left behind.

Fortunately the foundation has been set. Canada has a long history of creating and supporting the talent in quantum computing with $1-billion being invested in quantum science between 2012 and 2022. Furthermore, in the 2021 federal budget, $360-million was announced for Canadas National Quantum Strategy, and the Council of Canadian Academies estimates that quantum technologies could account for 3 per cent of Canadas GDP by 2045. In its April budget, the Canadian government announced it will invest $2.4-billion in AI infrastructure to catch up with other countries a field in which we were originally the leaders.

Lets not make the same mistakes for quantum and invest in quantum data centres early.

Link:

Opinion: As artificial intelligence rises, data-centre costs spiral. Quantum is the solution - The Globe and Mail