Category Archives: Artificial Intelligence

The US and its allies have found themselves in the middle of an AI arms race, with the prize of decision dominance on the battlefield for whoever gets there first.

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Artificial intelligence (AI) is widely recognised as a vital military capability that will only grow in importance in the era of multi-domain operations (MDO).

But what does this mean in practical terms, and how will the technology change the modern battlefield?

In the MDO concept also known as Joint All-Domain Command and Control (JADC2) platforms and systems across land, sea, air, space and cyber will interact and reinforce one another.

To make this possible, militaries will process and exploit vast reams of data, meaning that information processing and human-machine teaming will be essential. AI can provide vital advantages in all these areas, sifting through data at a rate far beyond any human operator.

When asked what brings the urgency to this space today, defence sources stress that there is little choice. Commercial technological innovations in AI have led to rapid, transformative changes across all service branches for all major powers.

Aneesh Kothari, vice president of marketing at Systel, a manufacturer of rugged computers, highlights that the US Department of Defenses Third Offset Strategy, for instance, holds that rapid advances in AI along with robotics, autonomy, big data and increased collaboration with industry will define the next generation of warfare.

We are in the middle of an AI arms race, and the end goal is decision dominance on the battlefield, Kothari said, noting that the same impulses are driving US allies and their adversaries.

AI enables operators to move past the limits of human capacity for mission-critical data-processing workloads. It reduces a significant degree of risk to personnel on the battlefield, such as the increasing ability to deploy uncrewed vehicles.

There is a wide range of programmes aiming to exploit such advances. One example is the UK Royal Air Forces Nexus Combat Cloud, which allows data from any sensor on any platform in a given operating space to be processed in real-time. The service has also advanced a swarming drone capability through the Alvina programme.

The area has also naturally become a growing focus for industry. BAE Systems, for instance, has worked on AI in a range of areas, with some of this coming through Defense Advanced Research Project Agency (DARPA) programmes.

Such work includes MindfuL, software that can independently audit Machine Learning-based systems, helping build trust in the technology, which will be crucial as militaries boost their focus on human-machine teaming.

BAE Systems is also developing the Multi-domain Adaptive Request Service (MARS) for DARPA, which will enable semi-autonomous multi-domain mission planning.

Michael Miller, technical area director for BAE Systems FAST Labs, said that MARS significantly increases available resources, enabling battle managers to solve unforeseen requirements in a dynamic tactical environment rapidly. Crucially, the system empowers human operators, an essential element of AIs practical utility on the battlefield.

The beauty of it is that it actually allows the human to make that final decision; it helps them find important capabilities and lets them decide which is the one they prefer, Miller explained.

AI and machine learning will help not just with data processing but also managing that data.

Fundamental to MDO or JADC2 is that in great power competition, communications will not be as assured as they once were in fact, they will be under attack.

Data must be moved judiciously, while forward forces will be dispersed, disaggregated and sometimes disconnected, said Jim Wright, technical director for intelligence, surveillance and reconnaissance systems at Raytheon Intelligence & Space.

Against this backdrop, Raytheon is working on architectures in which cognitive agents manage the data flow, he said, considering the commanders intent, how the battlefield is evolving, and the threats to communications, then using this information to determine how data should be placed.

Wright noted that AI/ML would support not just data processing, but works itself into the management of data around the network.

Nevertheless, the US and its allies dont operate in isolation. As they develop their capabilities, so do potential rivals, most obviously China.

John Parachini, a senior international defence researcher at the RAND Corporation, pointed to several ways the country is applying the technology, including domestic security.

China is also making significant progress in applying AI to uncrewed vehicles, he noted. Likewise, Russia has made significant advances, particularly in the ground domain. However, the robotics must fit in with what a military force is trying to do and the environment in which it operates.

Other countries have also made substantial progress, including Israel and Turkey. Its when the systems are used in the field that you see the successes and failures which is the real way that leapfrog advances are made, Parachini said, pointing to the use of Turkish drones in Syria and other regions.

Its those experiences that will provide the lessons learned that will allow them to improve their capabilities, he argued.

AI and humans have complementary strengths and weaknesses. While AI provides unrivalled data processing and management capabilities, humans can introduce a different perspective and intuition.

When these are combined, the result is an increasingly resilient capability. For example, Miller points to tools like Google Maps, which develop a new solution if a human deviates from a route.

Similarly, in defence applications, human intuition still matters, he said: human understanding of intangibles, things that the algorithm itself cant contemplate.

Machines and humans have complementary strengths and weaknesses, Kothari noted. We must align these in the most productive way. While machines have exponentially faster abilities to crunch data, human intuition will remain critical for tactical decision making.

The human ability to see all the shades of grey, complemented by the machines ability to see black and white incredibly quickly and accurately, is a very powerful combination and a winning combination for the nation that gets it right.

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Artificial intelligence becomes the critical enabler of future operations (Studio) - Shephard News

Edge computing is witnessing a significant interest with new use cases, especially after the introduction of 5G. The 2021 State of the Edge report by the Linux Foundation predicts that the global market capitalization of edge computing infrastructure would be worth more than $800 billion by 2028. At the same time, enterprises are also heavily investing in artificial intelligence (AI). McKinseys survey from last year shows that 50% of the respondents have implemented AI in at least one business function.

While most companies are making these tech investments as a part of their digital transformation journey, forward-looking organizations and cloud companies see new opportunities by fusing edge computing and AI, or Edge AI. Lets take a closer look at the developments around Edge AI and the impact this technology is bringing on modern digital enterprises.

AI relies heavily on data transmission and computation of complex machine learning algorithms. Edge computing sets up a new age computing paradigm that moves AI and machine learning to where the data generation and computation actually take place: the networks edge. The amalgamation of both edge computing and AI gave birth to a new frontier: Edge AI.

Edge AI allows faster computing and insights, better data security, and efficient control over continuous operation. As a result, it can enhance the performance of AI-enabled applications and keep the operating costs down. Edge AI can also assist AI in overcoming the technological challenges associated with it.

Edge AI facilitates machine learning, autonomous application of deep learning models, and advanced algorithms on the Internet of Things (IoT) devices itself, away from cloud services.

Also read: Data Management with AI: Making Big Data Manageable

An efficient Edge AI model has an optimized infrastructure for edge computing that can handle bulkier AI workloads on the edge and near the edge. Edge AI paired with storage solutions can provide industry-leading performance and limitless scalability that enables businesses to use their data efficiently.

Many global businesses are already reaping the benefits of Edge AI. From improving production monitoring of an assembly line to driving autonomous vehicles, Edge AI can benefit various industries. Moreover, the recent rolling out of 5G technology in many countries gives an extra boost for Edge AI as more industrial applications for the technology continue to emerge.

A few benefits of edge computing powered by AI on enterprises include:

Implementation of Edge AI is a wise business decision as Insight estimates an average 5.7% return on Investment (ROI) from industrial Edge AI deployments over the next three years.

Machine learning is the artificial simulation of the human learning process with the use of data and algorithms. Machine learning with the aid of Edge AI can lend a helping hand, particularly to businesses that rely heavily on IoT devices.

Some of the advantages of Machine Learning on edge are mentioned below.

Privacy: Today, information and data being the most valuable assets, consumers are cautious of the location of their data. The companies that can deliver AI-enabled personalized features in their applications can make their users understand how their data is being collected and stored. It enhances the brand loyalty of the customers.

Reduced Latency: Most of the data processes are carried out both on network and device levels. Edge AI eliminates the requirement to send huge amounts of data across networks and devices; thus, improve the user experience.

Minimal Bandwidth: Every single day, an enterprise with thousands of IoT devices has to transmit huge amounts of data to the cloud. Then carry out the analytics in the cloud, and retransmit the analytics results back to the device. Without a wider network bandwidth and cloud storage, this complex process would turn it into an impossible task. Not to mention the possibility of exposing sensitive information during the process.

However, Edge AI implements cloudlet technology, which is small-scale cloud storage located at the networks edge. Cloudlet technology enhances mobility and reduces the load of data transmission. Consequently, it can bring down the cost of data services and enhance data flow speed and reliability.

Low-Cost Digital Infrastructure: According to Amazon, 90% of digital infrastructure costs come from Inference a vital data generation process in machine learning. Sixty percent of organizations surveyed in a recent study conducted by RightScale agree that the holy grail of cost-saving hides in cloud computing initiatives. Edge AI, in contrast, eliminates the exorbitant expenses incurred on the AI or machine learning processes carried out on cloud-based data centers.

Also read: Best Machine Learning Software in 2021

Developments in knowledge such as data science, machine learning, and IoT development have a more significant role in the sphere of Edge AI. However, the real challenge lies in strictly following the trajectory of the developments in computer science. In particular, next-generation AI-enabled applications and devices that can fit perfectly within the AI and machine learning ecosystem.

Fortunately, the arena of edge computing is witnessing promising hardware development that will alleviate the present constraints of Edge AI. Start-ups like Sima.ai, Esperanto Technologies, and AIStorm are among the few organizations developing microchips that can handle heavy AI workloads.

In August 2017, Intel acquired Mobileye, a Tel Aviv-based vision-safety technology company, for $15.3 billion. Recently, Baidu, a Chinese multinational technology behemoth, initiated the mass-production of second-generation Kunlun AI chips, an ultrafast microchip for edge computing.

In addition to microchips, Googles Edge TPU, Nvidias Jetson Nano, along with Amazon, Microsoft, Intel, and Asus, embarked on the motherboard development bandwagon to enhance edge computings prowess. Amazons AWS DeepLens, the worlds first deep learning enabled video camera, is a major development in this direction.

Also read: Edge Computing Set to Explode Alongside Rise of 5G

Poor Data Quality: Poor quality of data of major internet service providers worldwide stands as a major hindrance for the research and development in Edge AI. A recent Alation report reveals that 87% of the respondents mostly employees of Information Technology (IT) firms confirm poor data quality as the reason their organizations fail to implement Edge AI infrastructure.

Vulnerable Security Feature: Some digital experts claim that the decentralized nature of edge computing increases its security features. But, in reality, locally pooled data demands security for more locations. These increased physical data points make an Edge AI infrastructure vulnerable to various cyberattacks.

Limited Machine Learning Power: Machine learning requires greater computational power on edge computing hardware platforms. In Edge AI infrastructure, the computation performance is limited to the performance of the edge or the IoT device. In most cases, large complex Edge AI models have to be simplified prior to the deployment to the Edge AI hardware to increase its accuracy and efficiency.

Virtual assistants like Amazons Alexa or Apples Siri are great benefactors of developments in Edge AI, which enables their machine learning algorithms to deep learn at rapid speed from the data stored on the device rather than depending on the data stored in the cloud.

Automated optical inspection plays a major role in manufacturing lines. It enables the detection of faulty parts of assembled components of a production line with the help of an automated Edge AI visual analysis. Automated optical inspection allows highly accurate ultrafast data analysis without relying on huge amounts of cloud-based data transmission.

The quicker and accurate decision-making capability of Edge AI-enabled autonomous vehicles results in better identification of road traffic elements and easier navigation of travel routes than humans. It results in faster and safer transportation without manual interference.

Apart from all of the use cases discussed above, Edge AI can also play a crucial role in facial recognition technologies, enhancement of industrial IoT security, and emergency medical care. The list of use cases for Edge AI keeps growing every passing day. In the near future, by catering to everyones personal and business needs, Edge AI will turn out to be a traditional day-to-day technology.

Read next: Detecting Vulnerabilities in Cloud-Native Architectures

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Edge AI: The Future of Artificial Intelligence and Edge Computing | ITBE - IT Business Edge

NEW YORK, Aug. 24, 2021 /PRNewswire/ -- TheDeloitte AI Institutetoday unveiled a new report that examines the most compelling business use cases for artificial intelligence (AI) across six major industries. The report, "The AI Dossier," helps business leaders understand the value AI can deliver today and in the future so that they can make smarter decisions about when, where and how to deploy AI within their organizations.

"The AI Dossier" illustrates use cases across six industries, including consumer; energy, resources and industrial; financial services; government and public services; life sciences and health care; and technology, media and telecommunications. For each industry, the report highlights the most valuable, business-ready use cases for AI-related technologies examining the key business issues and opportunities, how AI can help, and the benefits that are likely to be achieved. The report also highlights the top emerging AI use cases that are expected to have a major impact on the industry's future.

"Artificial intelligence has made the leap to practical reality and is quickly becoming a competitive necessity. Yet, amidst the current frenzy of AI advancement and adoption, many leaders are questioning what AI can actually do for their businesses," said Nitin Mittal, U.S. AI co-leader and principal, Deloitte Consulting LLP. "The AI Dossier can help these leaders understand the value AI can deliver and how to prioritize their investment in AI, today and in the future."

Deloitte's "State of AI in the Enterprise, 3rd Edition"study found that 74% of businesses are still in the AI experimentation stage with a focus on modernizing their data for AI and building AI expertise through an assortment of siloed pilot programs and proofs-of-concept, but without a clear vision of how all the pieces fit together. By contrast, only 26% of businesses are focused on deploying high impact AI use cases at scale, which is where AI can create real value.

"While AI adoption rates and maturity vary widely across industries, AI is driving new levels of efficiency and performance for businesses of all sizes," said Irfan Saif, U.S. AI co-leader, Deloitte Risk & Financial Advisory, and principal, Deloitte & Touche LLP. "Organizations have the opportunity to unlock the full potential of AI when they embrace it and deploy it at scale throughout their enterprise."

Six ways AI creates value for business

The report looks across all the industry-specific use cases to identify six major ways AI can create value for business:

The Deloitte AI Institute supports the positive growth and development of AI through engaged conversations and innovative research. It also focuses on building ecosystem relationships that help advance human-machine collaboration in the Age of With, a world where humans work side-by-side with machines.

About Deloitte

Deloitte provides industry-leading audit, consulting, tax and advisory services to many of the world's most admired brands, including nearly 90% of the Fortune 500 and more than 7,000 private companies.Our people come togetherfor the greater good and work across the industry sectors that drive and shape today's marketplace delivering measurable and lasting results that help reinforce public trust in our capital markets, inspire clients to see challenges as opportunities to transform and thrive, and help lead the way toward a stronger economy and a healthier society. Deloitte is proud to be part of the largest global professional services network serving our clients in the markets that are most important to them.Building on more than 175 years of service, our network of member firms spans more than 150 countries and territories. Learn how Deloitte's more than 330,000 people worldwide connect for impact at http://www.deloitte.com.

Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee ("DTTL"), its network of member firms, and their related entities. DTTL and each of its member firms are legally separate and independent entities. DTTL (also referred to as "Deloitte Global") does not provide services to clients. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the "Deloitte" name in the United States and their respective affiliates. Certain services may not be available to attest clients under the rules and regulations of public accounting. Please see http://www.deloitte.com/aboutto learn more about our global network of member firms.

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SOURCE Deloitte AI Institute

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Deloitte AI Institute Unveils the Artificial Intelligence Dossier, a Compendium of the Top Business Use Cases for AI - KPVI News 6

Free Guy bills itself as a comedy, but it exists in a world in which Ryan Reynolds doesnt exist. Which, of course, makes it a tragedy. The movie solves this problem by building its own Ryan Reynolds out of code, inside a video game. A reasonable response to such a terrible lack. But,Guy (Reynolds) is more than just a simple NPC. He's self-aware and, in a way, alive.

In truth, the emergence of Guy as a fully-fledged awareness inside the game wasnt wholly directed. Instead, he blossomed from a set of prior conditions, much like the IOs in Tron Legacy, having learned from his experiences inside the game environment.

If anyone has yet built living entities out of zeroes and ones, theyre keeping that information to themselves (a terrifying prospect) but artificial intelligences have long been a staple of video games. And theyre getting smarter.

WOULD YOU LIKE TO PLAY A GAME?

For decades, video games have been a ready benchmark for testing the latest artificial intelligences. First, we need a quick primer in defining terms. While the term "artificial intelligence"conjures images of replicants and Skynet, it can refer to any number of systems designed to help a computer or machine complete a task.

The simplest of these systems is reactive; a machine takes in a set of conditions and, based on its programming, determines an action. These sorts of AI are common in video games. An enemy may attack once youve entered into a pre-determined perimeter then, depending on conditions set by the game designers, its behavior plays out. Maybe it continues to attack until you or it are defeated. Maybe it attacks until its health bar reaches a critical low and then retreats.

To the player, the game character appears to be making decisions, even while its essentially navigating a flowchart. And this sort of AI will behave in the more-or-less the same wayeach time you encounter it. It isnt thinking about what happened in the past or what might happen in the future, its simply taking existing conditions, bumping them up against potential actions, and selecting from those available.

It could be argued that these types of AI have existed since the dawn of video games. Even the computer opponent in Pong took a measure of the playing field and altered position in order to better defend the ball. Were that not the case, the opposing cursor would simply move randomly along the field, and the game would be no fun.

More advanced AI have limited memory, they store at least some of their past interactions and use that knowledge to modify future behavior. This is closer to what we think of when we think of AI, a machine that not only thinks, but learns and then thinks differently.

Much was made of the Nemesis system in Warner Bros. Middle-earth: Shadow of Mordor, when the game dropped. Instead of seemingly brainless adversaries which could be defeated through sheer force of will, Shadow of Mordor offered something closer to life. Enemies remember you, hold grudges, and alter their tactics based on yours. They have limited memory, and they learn from you. It made for a different sort of game-play experience by making the other characters a little more real.

In 2019 Googles AlphaStar AI, built by their DeepMind division, set about rising the ranks of StarCraft II. The folks at DeepMind chose StarCraft because of its complexity when compared to games like chess.

Chess, with its comparatively limited tokens and move-types, still boasts a truly staggering number of possibilities. StarCraft ratches up the complexity, making it a reasonable next step for game AI. The team started by feeding AlphaStar roughly a million games played by human players. Next, they created an artificial league, pitting versions of AlphaStar against one another. The system learned.

Eventually, AlphaStar was let loose on some of the best StarCraft players in the world and quickly rose in the ranks. While it didnt beat everyone, it did place in the top half-a-percent, and thats even with its speed capped to match what humans are capable of. All of this was possible due to AlphaStars ability to take in information and learn from it, refining its process as it goes.

Most modern AI are built on this model. They take in a data set, either provided in advance or learned through interaction, and use it to build a model of the world. Or, at leasta model of the thin slice of the world with which they are concerned. Image detection programs are trained on previously viewed images. They look for patterns and, over time, get better at recognizing novel images for what they are. Chatbots do something similar, cataloging the various conversations they have with people or with other bots, to improve their responses. Each of these programs can become skilled at limited tasks, matching or even exceeding human ability.

Those limits, the boundaries within which AI operate, are precisely what make them good at their jobs. Its also what prevents them from awareness. AlphaStar might be good at StarCraft, but it doesnt enjoy the thrill of victory. For that, wed need

THEORY OF MIND AND FULL AWARENESS

We cant get a robot uprising, a mechanical Haley Joel Osment, or a digital Ryan Reynolds without stepping up our game. Building truly intelligent machines requires that they have a theory of mind, meaning they understand there are other thinking entities with feelings and intentions.

This understanding is critical to cooperation and requires a machine to not only understand a specific task, but to more fully understand the world around them. Today, even the best AI are operating solely from the information theyve been given or have gleaned from interaction.

The goal is to have machines capable of taking in the complex and seemingly random information in the real world and making decisions similar to the ones we would make. Instead of delivering commands, they would pick up on social queues, unspoken human behavior, and unpredictable chance occurrences. We, likewise, should be able to have some understanding of their experience if it can be called experience if we hope to collaborate meaningfully.

Alan Winfield, a professor of robot ethics at the University of West England, suggests one way of accomplishing an artificial theory of mind. By allowing machines to run internal simulations of themselves and other actors (machines and humans), they might be able to play out potential futures and their consequences. In this way, they might gain an understanding that other entities exist, have motives and intentions, and how to parse them.

One major difficulty is the stark reality that we dont really understand theory of mind even in ourselves. So much of what our brains dohappens behind the scenes, and its the amalgamation of those processes which likely result in awareness.

This, too, is the major hurdle in designing machines with full awareness. But pushing toward a machine theory of mind might help us get closer. If human awareness is an emergent property of countless simpler processes all working together, that might also be the path for achieving awareness in machines or programs. Not with a flash, but slowly and by degrees.

True artificial intelligence of the kind seen in novels, movies, and television shows is probablya long way off but the seeds may already be planted, maybe even in a video game you've been playing.

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How close are we to Free Guy's digital awareness? The Science Behind the Ficiton - SYFY WIRE

Artificial Intelligence has catapulted in recent years, and the advancements being made in this field make me feel as if it wont be long before we have robots walking amongst us all the time! There was a point in time when the only forms of robots that we could see were toys or vacuum cleaners, or if we were lucky an AI-enabled lawnmower in some tech-trendy individuals backyard! But we have come a long long way since then. From a basketball-playing Japanese robot at the Tokyo 2021 Olympics to a Microsoft-powered robot that cleans up littered cigarette butts on the beach the potential and scope of robots grow exponentially day by day. The world at large is slowly moving away from the perception of robots as evil beings who want to take over the Earth, and accepting that they may have vast and undeniable utility in even our day-to-day lives. Whether programmed for fun or functionality, robots are always intriguing to watch and examine! And, weve curated some really innovative ones that completely blew our minds away!

At the Tokyo 2021 Olympics, world-class athletes were showcasing their talent, but a robotstole all the limelight during a basketball game between the U.S. and France. Demonstrating the early stages of the machine-dominated dystopian future, the seven-foot robot developed byToyota engineers scored a perfect three-pointer and half-court shot. The eerily designed robot took to the center stage at halftime break during a showdown game. The Toyota engineers created this free throw shooting robot in their free time over the last couple of years and at the game the smart machine beat human players shot for shot. It perfectly landed an easy free throw, a three-pointer, and a flawless half-court shot (just like Stephen Curry) in tandem to wow the crowd!

In collaboration with OTTOBO Robotics, product and car designer Berk Kaplan developed a concept design for a task robot that integrates smart technology to streamline ergonomics and package-carrying flexibility. During the beginning stage of the concept design phase, Kaplan first conducted his own research to settle on the overall mood and personality of the robot. Following the research period, Kaplan sat down to sketch outlines of his robot in development, toying around with practical elements and aesthetic touches. The first proposal envisioned the robot with both a hard outer shell and inner core, giving it a tough, hardworking personality and weighty body. Where the first proposal found durability in a tough exterior and interior, the third proposal from Kaplan wrapped the robot in a soft outer shell to cover the robots soft interior core. The second proposal, which Kaplan and OTTOBO Robotics ultimately chose as the concept designs final form, conceived the robot with a soft outer shell and hard inner core for a cushioned tactile experience, outfitting the robot with a friendly and approachable disposition.

Xiaomi, a Chinese tech company, recently unveiled more 3D renders of their own Quadruped robotic creation, CyberDog. Currently, the bio-inspired, four-legged robot has been engineered as a robotic companion whose future technical capabilities are still in development. In a recent press release from Xiaomi, its said that CyberDog comes complete with AI interactive cameras [and sensors], binocular ultra-wide-angle fisheye cameras, and Intel RealSense D450 Depth module, and can be trained with its computer vision algorithm.

Oliver is a collaborative robot that can operate both automated and manual delivery services. Smart technology equips Oliver with the know-how to handle autonomous delivery outings most likely contained within indoor spaces like warehouses and office buildings. Goods can be placed inside of Oliver the same way items are carried by utility carts and additional packages can be attached to Olivers rear trailer. Once the goods are packed away, a touchscreen display allows users to orient Oliver and schedule their deliveries. The vertical carrying space automatically rises at each delivery destination to make the unloading process more manageable. Besides automated delivery services, Oliver can operate as a conventional utility cart if users would prefer to deliver their goods on foot.

This robot may look like theMarsrover, but its a unique cigarette bud collecting bot designed to clean up the litter on beaches. Called the BeachBot (BB), this cute little four-wheeled machine was developed by Edwin Bos and Martijn Lukaart of TechTics. The duo got livid with the amount of trash (cigarette butts in particular) on the Scheveningen Beach in Holland and wanted to design arobotthat could help clean up the mess. Thats how the 2.5-feet wide BeachBot came into existence, looking to navigate the beaches on its bloated wheels that dont create any marks on the sand. The battery-powered bot has an AI brain that uses image-detection software to identify the butts and then pick them up with its gripper arms. The collected trash is then stored in the onboard compartment to dispose of later.

The KODA Robot Dog holds the title for being the first high-end domestic robot-dog running on a decentralized blockchain network, with its own brain an 11 teraflop processor capable of A.I. machine-learning. The dog-type quadruped robot relied on a decentralized network to share data and optimize behavior, making all KODA dogs smarter by relying on a hive-mind of sorts. For example, a KODA dog in Phoenix can use the knowledge it automatically receives from other KODAs that are based in colder climates, like Anchorage, Alaska or Toronto, Canada, Harden mentions to Yanko Design. Without ever having set foot on ice, the KODA in Phoenix will learn how to avoid slipping. This includes warning its owner as well. Armed with that incredibly powerful software, Whipsaws design took an interesting-yet-logical decision of ensuring the KODA robot dog (as intelligent and capable as it was) still retained a friendly, cute demeanor.

Keunwook Kim designed Post-Plant, a collection of non-humanoidrobots that respond to and move through non-verbal, physical interaction. Following a period of researching how humans can read emotion from non-verbal cues, Kim gathered that arousal (dynamic energy), valence (intrinsic attractiveness), and stance (visual disposition) can each be interpreted as signs for emotional analysis. Applying this information to Post-Plant, Kims non-humanoid robots do not express emotion through facial expression, but through movement and changing forms. Built into each one of his Post-Plant robots, Kim incorporated a motor interface that combines an input and output system, registering when the robot is touched and responding with movement.

Imagine if R2-D2 got a 2021 makeover? Well, BEBOP Design did something like thatthey took the concept and gave it a sleek makeover to give us all Information Robot! This is an autonomous robot designed specifically for the Korean startup Zetabank that aims to make human lives safer and healthier with the help of robots. Zetabank has a range of robots and this is their second collaboration with BEBOP. The companys mission is to improve our lives using artificial intelligence. Their Disinfectant Robot, Hospitality Robot, and Untact Robot are all designed keeping in mind how they can maximize utility and bring practicality to make our day-to-day more efficient. Continuing that legacy is Information Robot which is created as a service platform for digital interactions building upon the Hospitality Robots intelligence. These digital interactions are enhanced by the robots autonomous movement in various commercial and residential spaces.

Eggos mission is simple to give you a robot pet that is always by your side and provides a positive experience to you. This egg-shaped companion lets you raise a pet online or offline without taking away from the experience. It has a simple design, minimal interface, and an organic shape that invites interaction. Eggo moves autonomously by grasping the terrain through a camera. The smart pet also automatically goes to charge itself when the battery is low and I honestly wish my phone did the same thing. Even though it is a robot, designer Hyunjae Tak made sure to include an emotional side so Eggo can express how it is feeling through the LED colors which are extremely important when interacting with children. It uses the inner wheel to move on its own and actually forms a unique personality according to how you take care of it just as you would with a real-life pet!

This gadget can be fixed to the wearers forehead, who is too busy looking down at the smartphone. You know where we are heading, dont you? Yes, the 3rd Eye keeps a lookout on obstacles as you walk on the street, with the phone screen keeping you preoccupied. The inbuilt ultrasonic sensor automatically detects whenever your head is tilted down to check the phone and beeps a warning buzz when a hazard is detected up to a distance of one meter. This niche creation is a part of Minwooks Innovation Design Engineering degree at Londons Royal Imperial College of Art and Imperial College. The designer sees this evolution of human beings as a sarcastic imagination for him to do something creative. He labels the evolution as phono sapiens, and understandably so, seeing how we are so deeply lost in the world of the internet. How do you identify a phono sapien? With their forward-leaning neck vertebrae resulting in the dreaded turtle neck syndrome!

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Intriguing and Inventive Robot Designs that prove artificial intelligence is here to stay + make the world a better place! - Yanko Design

As recent events have shown, military decision-making is one of the highest-stakes challenges in the world: Diplomatic relations are at stake; billions of dollars of tax-funded budgets are in the balance; the safety and well-being of thousands of military and civilian personnel around the globe are on the line; and above all, the freedom and liberty of the United States and its more than 330 million citizens must be protected. But with such immense stakes comes an almost unfathomably large amount of related data that must be taken into account. Whether it is managing population health in an increasingly complex and connected world, or managing decisions on the network-centric battlefield, standalone humans are proving insufficient to harness the data, analyze it, and make timely and correct decisions.

Spanning six branches and upward of 1.3 million active duty military personnel on all seven continents, how can all of the data points from dictates from the commander-in-chief to handwritten notes on the deck of an aircraft carrier be taken into account? In matters of national security, speed and reliability in decision-making and avoiding technological surprises or being caught off guard by the nations political rivals require massive real-time analysis and first and second order thinking that includes the complexities of human behavior.

Consider all of the stakes and moving parts facing the leadership at a large domestic military base during the recent COVID-19 pandemic. Concerns of COVID-19 did not just need to consider the base personnel, but also the behavior of the civilians in the surrounding counties, as people from throughout the region, military and civilian contractors alike, were coming and going daily. The information necessary to consider starts with infection and hospitalization rates, but also includes behavior monitoring (and influencing) as well as staying up to date with steps being taken by local, regional and state officials to monitor the virus and limit its spread. With so many moving parts, it is very difficult to stay up to the minute on everything and to determine the right decision with any degree of certainty.

The answer to this guesswork and analysis paralysis lies in the capabilities of artificial intelligence and machine learning. If the military continues to waste too much time with human hours of effort and analysis that could be handled by machines, that could lead to danger and even death of military personnel or civilians. At the heart of complex systems, such as the U.S. military, there is a critical tipping point where the systems are so complex that humans can no longer track them. But AI solutions are capable of delivering up-to-the-minute data modeling, considering all factors at play and second and third order consequences, that can present tangible, data-driven intelligence that takes actions far beyond the limitations of linear human minds. Perhaps the biggest benefit is the confidence to avoid the negative publicity from the podium moment, when asked to justify decisions. Decision-makers can confidently move beyond relying on hunches and instead identify data based on sub-indexes, models from experts, and simulations specific to that day and the circumstances specific to each facility.

When President Biden was recently called onto the carpet to explain the rapid fall of Afghanistan in nine days, he should have had an AI that could at least explain the data, the models and weights that fed the analysis, conclusions and decisions based on the belief that the 300,000 strong Afghan army would be able to hold off the 60,000 Taliban fighters long enough for an orderly withdrawal. Journalists would then be free to question the data sources, the models or the weightings, but not the president, who would be relying on these systems for his judgment. But more importantly, such a system would have certainly predicted this rapid fall in its Monte Carlo distribution of potential outcomes, and would have generated counter measures and cautions.

Without a deeper commitment to AI, the military risks missing out on intelligence that transcends classified, siloed and otherwise restricted information without compromising security. One of the biggest challenges to high-stakes decision-making in the military is silos of classified information, making it difficult or impossible for every party to know every factor that is shaping the situation.

Using AI and machine learning solves this challenge safely. Rather than dumping disparate data from various branches of the military and clearance level into one gigantic data lake, it is possible to leave all the data safely and securely where it is, and train a machine to know and inform the human decision-makers that the data exists. AI is capable of processing not only all of the information in the corpus, but it is also able to know which parties do and do not have clearance to each individual piece of data. In matters of classified information, it can tell different personnel that the information exists, and direct these individuals to the authority qualified to disclose it.

Capabilities like these can be readily applied to large, complex military undertakings, featuring processes, decisions and volumes of information. For instance, when a new aircraft carrier is being built, management requires information in hand-written reports. It is difficult for the naked eye to tell if the project is on time or on budget because of the heavy reliance on human judgment. If any human assessment is just a fraction off, it can massively impact the whole project.

Recent challenges that factor in the vagaries of human behavior illustrated starkly by COVID-19 and the withdrawal from Afghanistan, beg for the rapid analysis and creative input of machine learning systems. From digestion and quantification of countless data points to absorbing and cataloging knowledge of experts who will not always be around to help with predictive modeling of circumstances with dozens of variables, this amplified intelligence is the key to better outcomes.

Richard Boyd is CEO at Tanjo, a machine learning company.

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COVID-19 showed why the military must do more to accelerate machine learning for its toughest challenges - C4ISRNet

Former Premier League midfielder Matt Oakley, seen here in action for Southampton in 1999, is a key backer of the AI tool

This does not mean that the human touch and experience needed to make these decisions are totally redundant

Artificial Intelligence, also known as AI, is a subject that has been spoken about in the world a lot, these days. There have been quite a few doomsday predictions where it has been projected that the machines will totally take over the functions of human beings as well.

Be that as it may, AI is defined as the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.

The term AI may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

Uses of AI

There are four types of Artificial Intelligence. They are (i) Reactive machines, (ii) Limited memory, (iii) Theory of mind, and (iv) Self-awareness.

AI enhances the speed, precision, and effectiveness of human efforts. In financial institutions, AI techniques can be used to identify which transactions are likely to be fraudulent, adopt fast, and accurate credit scoring, as well as automate manually intense data management tasks.

There has been wide speculation as to how this can be used in sports.

Take smarter decisions?

In an earlier column, this writer presented an article regarding the use of a vest for data collection of athletes.

And, if the data collected during these matches, can they be used to feed the machines. Then could AI be used to make smarter decisions regarding players, by the coaches, clubs, and national sports bodies.

This has been the question that has been discussed and is being discussed at all levels of sports in the more advanced countries.

The answer to that question seems to be yes, judging from the results of the use of AI in the England Premier League football during last season. But one successful prediction is not conclusive evidence in any circumstance. And the accuracy of its predictions still makes reads rather scary reading.

Real Analytics

Former Southampton midfielder Matt Oakley and Professor Ian McHale of the University of Liverpool are behind the recruitment tool, Real Analytics, that they believe will revolutionise the way in which clubs obtain and retain players. Oakley has a very impressive background in football after having played for a number of Premier League clubs in his playing days.

Oakley, who made nearly 700 senior appearances for the likes of Southampton and Leicester, says: From my perspective as a former player, seeing what Ian can do is so exciting. This is completely different, that ability to predict the impact of results on a specific team. But it also helps in knowing when to sell a player. The analysis gives an unbiased view, all based on what a player does on the pitch.

Scouts should use data and vice versa

But as Oakley says: There is a definite resistance from some Managers towards data. I have seen it, but we are striving to bridge the gap between data and football. We have data on 150 leagues and 40,000 players. It is about turning that into useful information. At the moment, we believe people arent using it to its full advantage. That is not to say the traditional scout should be made redundant. Scouts should use data and data should use scouts. For example, a man in the stand can tell you more about body language and when a players head drops. We know there is more to it than numbers.

Scarily correct predictions

Professor Ian McHale of the University of Liverpool who created the technology that tells you what will happen, not what has happened

They spoke then of how, last summer, they were asked to run a player impact report on Pierre Emerick Aubameyang, prior to Arsenal football clubs decision to award him a lucrative new contract, worth 55 million (Rs. 15 b) over three years.

After all, Aubameyang had been their leading scorer and talisman at the club. Oakley and Prof. McHale however warned how keeping the striker at the club was perhaps not a prudent move.

Their system, Real Analytics, predicted that Arsenal would finish in an average of eighth position in the Premier League with Aubameyang in the squad, and ninth without him. It also projected that they would score 55 goals with, and 50 without. Arsenal finished eighth, with a goals tally of 55.

How does it work?

So how does this work? As McHale explains, all the readings are in the data itself.

The numbers that go into it are called event data. Every match has around 2,000 events. A pass is not just recorded as a pass. We have the x-y co-ordinates of where the pass came from and where it went and who it went to.

Our AI engine learns the value of every action in terms of what it contributes to the likelihood of that possession ending in a goal. Every pass, every tackle, every interception, everything is given a positive or negative score.

Originally from gambling industry

Real Analytics claims to be the future of decision making in football

He further went onto say, It can be used for coaching to look at the moments where you have added value, or it can deem that you have taken value away. You can see how some players, who might be completing lots of passes, actually impact negatively on the team.

But when it comes to recruitment, it is no use knowing how good a player was last week. We need our tools to be predictive. We want to know how good he will be next year, or in five years time. It turns out things like pass completion percentage reveal little about a players future performances.

We have been working on these types of models for 20 years. They were originally built for the gambling industry and designed for forecasting the outcome of matches. We realised that all the tools we were building, we could use them for the football industry, with a few tweaks.

Conclusion

This is only one of the predictions of a number of others that came in correctly as predicted by Real Analytics. This does not mean that the human touch and experience needed to make these decisions are totally redundant.

But what it does mean is that Artificial Intelligence or AI can be made use of by the humans to make better-informed decisions.

Continued here:

Use of Artificial Intelligence (AI) in football - nation.lk - The Nation Newspaper

Corin Stone is a Scholar-in-Residence and Adjunct Professor at the Washington College of Law. Stone is on leave from the Office of the Director of National Intelligence (ODNI) where, until August 2020, she served as the Deputy Director of National Intelligence for Strategy & Engagement, leading Intelligence Community (IC) initiatives on artificial intelligence, among other key responsibilities. From 2014-2017, Ms. Stone served as the Executive Director of the National Security Agency (NSA).

(Editors Note: This article was first published by our friends at Just Security and is the second in aseriesthat is diving into the foundational barriers to the broad integration of AI in the IC culture, budget, acquisition, risk, and oversight.)

OPINION Several weeks ago, I wrote anarticlepraising the widespread, bipartisan support for the U.S. Innovation and Competition Act (USICA), which would dramatically expand federal government support for U.S. technological growth and innovation in the face of the global AI race.

In that article, I argued that for the Intelligence Community (IC) to take advantage of AI in this supportive environment, it must overcome several critical implementation challenges, and quickly. In particular, the IC must more rapidly and nimbly navigate U.S. government budget and acquisition processes, create a simple but effective risk assessment framework, and work with congressional overseers to streamline engagement and improve the partnership between Congress and the IC. Each of these areas is in dire need of radical re-imagining, without which any one of them could be the Achilles heel for AI in the IC. I will address each of these in my next few articles.

To successfully tackle any of these specific tasks, though, the IC must at the same time prioritize an issue much more intangible and nebulous its own culture. Culture is the ethos of an organization the beliefs, behaviors, values, and characteristics of a group that are learned and shared over many years. In the IC, there are several predominant cultures, all of which flow from the mission of the IC protecting the women and men on the front lines, defending U.S. national security, and delivering insights to policymakers at the speed of decision. This mission is a powerful and unifying force that naturally leads to important IC values and behaviors.

IC Culture Today

Intelligence operations uncovering foreign secrets and protecting assets, for example are inherently risky; they very often put people in harms way. If there is a leak of information related to an operation if the people involved, or the location or target of an operation, are exposed not only might the mission fail to collect the desired information, but someones life could also be in jeopardy. The extreme consequences of leaks are well understood, thanks to notorious spies likeRobert Hanssenand inside leakers likeEdward Snowden. But significant damage can also flow from what seem like merely small mistakes. If someone fails to make a connection between relevant information or forgets to check a database of known terrorists, for example, the results can be just as disastrous. Thus, the ICs high-stakes operations drive an enormous emphasis on security, preparation, and tradecraft, all of which help mitigate operational risk.

This same spirit manifests in enabling activities, like budget, acquisition, or human resources, through a focus on certainty of action and predictability of results. Enabling activities by some considered a negative term but one in which I take pride as a life-long enabler are somewhat removed from the pointy end of the spear but are no less critical to the ultimate success of the mission. Proper funding and resources, the right capabilities, skilled officers, legal approval, and the many other support activities are integral to successful operations.

In the field, risks are unavoidable operators cannot choose inaction to avoid those risks. Given that risks are inherent in what they do, they must accept the reality that risks are inevitable, and they must learn to manage those risks to get the huge payoff of successful operations. So, the focus is not on risk avoidance, it is on risk management what level of risk is acceptable for what level of intelligence gain?

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Back home, where most enabling activities are handled, risks are not seen as inevitable certainly not big ones. They are seen as avoidable, and subject to being minimized and mitigated. And some believe the best way to do that is by staying with tried-and-true standard operating procedures rather than experimenting with new approaches. Innovation is inherently risky. It can and will fail. Innovation is not mandatory, it is entirely avoidable. Therefore, if the tendency is to avoid risks, in most cases innovation will be avoided.

In addition to this instinct, there are compounding issues that discourage innovative change in enabling activities. First, there are practical difficulties: change is hard, messy, and requires resources that most offices cant spare. These concerns alone are big hurdles to clear. Second, innovative change means uncertainty in execution, accountability, and success. And that uncertainty leads to the risk that projects may fail, resulting in loss of money, reputation, or even position. Thus, control, compliance, and trust are paramount, and there is a strong aversion to things not invented here. Innovation is not particularly welcome in this environment and introducing new ideas can be an uphill battle, discouraging creativity in areas where it is needed most.

When it comes to budget and acquisition processes in particular which are critical to the ICs ability to quickly harness the power of AI capabilities the ICs risk-averse culture is also drawn in part from its reliance on Department of Defense (DOD) processes. And while DOD processes have aligned to IC needs in the past, they are based on decades-old approaches used for major systems acquisitions, like airplanes, aircraft carriers, and satellites. These are big ticket items that require minimal risk and significant time, structure, and certainty to move forward, because any failure could have costly consequences. But using this same approach for emerging technology like AI hampers the ICs ability to promptly obtain and use it before the underlying technologies become obsolete. In these cases, the IC must have the ability to try new things quickly, adjust on the fly, and accept some level of loss and failure as it continues to grow new technologies and processes. The IC must embrace a more flexible, innovative approach for AI even as that approach introduces more uncertainty.

Opportunity Born from Crisis

It is difficult, if not impossible, to create major cultural change in the absence of a compelling real-world problem. As leading change management expertJohn Kotterexplains, a sense of urgency helps others see the need for change and the importance of acting immediately. For example, prior to the COVID-19 pandemic, telework in the IC was virtually unheard of. Over the past 18 months, however, the IC not only achieved telework for many roles, telework proved to be so productive that many IC elements have now also changed their standing policies to allow it in certain roles going forward. Using that crisis to create a blueprint for new long-term approaches was key to jump-starting a real transformation.

Another striking example occurred after the September 11, 2001 attacks (9/11). Prior to 9/11, the IC culture around sharing and protecting information was heavily weighted toward jealously guarding information within agency stovepipes. This was a result of many things including the genuine need to protect sources and methods but it evolved in the extreme because it is easier to protect sensitive information if fewer people have access to it and, importantly, knowledge is power. After 9/11, though, IC culture slowly started to evolve to one that acknowledged the need to connect the dots and proactively share information with critical partners, even while protecting it. The need to share information more freely within the IC had beendocumented for decadesand executive branch policy already allowed information to go to those who had a need to know, but it took the crisis of 9/11 to really start to break through the cultural barriers to information sharing.

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Indeed, the IC now has created an entire information technology enterprise architecture groundbreaking for the U.S. government when the enterprise was started in 2011 founded on the concept that the IC agencies must work together and share information more easily than in the past. The IC Information Technology Enterprise (IC ITE) has been underway for a decade and is not yet complete, not solely due to its audacious technical goals but because it is breaking down cultural barriers, connecting the IC as never before, and pushing the IC together beyond its comfort level. Culture shifts slowly, and this one has had its detractors. But eventually over time there has become a general acceptance of and willingness to share information in ways that would previously have been unthinkable. The 9/11 crisis and subsequent terrorist attacks (and attempts) brought the responsibility to share information into immediate, sharp focus.

As I discussed in my previousarticle, one of the ICs crises today is the ubiquity of and access to AI around the globe. Sophisticated technology like AI is no longer available to only a handful of wealthy governments; it is now abundant and often easy to acquire, enabling smaller foreign governments and non-governmental actors alike to take advantage of it. If the IC does not modernize its approach to AI quickly, it is only the United States that will pay. The IC must turn this crisis into an opportunity to embrace a more flexible and innovative culture that supports the ICs ability to leverage AI tools at the speed of mission.

IC Leadership Action Required

Culture is driven from thetopthrough leadership actions, behaviors, and priorities that reverberate across every level of an organization. To grow an IC-wide culture of innovation, IC leaders must set the right vision and tone by affirmatively and publicly embracing outside-the-box thinking, articulating acceptable risk and expected failure rates, continuing to back innovators when projects go awry, and setting expectations for collaboration between innovators and practitioners in every area from budget to acquisition to operations.

In 2018, the Director of National Intelligence (DNI) made IC-wide innovation a top priority by creating an ODNIinnovation organizationto lead and support, among other things, cross-IC creativity and modernization to help pioneers find each other, to encourage the IC to leverage each others ideas, to provide advice to those hoping to contribute new ideas, and to train officers on innovation best practices. This organization also led the top IC strategic initiatives, including Augmenting Intelligence using Machines (AIM), which drove senior and subject-matter-expert coordination and collaboration across the IC on AI and machine learning activities to better align innovation, acquisition, and use of AI and emerging technology.

The AIM initiative was one of only six priorities the directors of every IC element very visibly embraced as imperative to the future of the IC. As a result, the AIM initiative had relative success in its first few years, bringing the community together and ensuring better coordination across the many AI initiatives. However, the innovation offices more foundational task of creating a broad IC culture that embraces innovation foundered. Chronic under-resourcing of those activities signaled that they were not, in fact, a priority, and subsequent changes in ODNI leadership and support resulted in minimal progress over the course of two years. That office was recently disbanded, leaving IC innovators to continue to fend for themselves and signaling a lack of strategic support and leadership for IC-wide innovation.

There are glimmers of hope, however. Innovation remains one of the ICs statedvalues, and despite the lack of coordinated approach, there are pockets of brilliant innovation across the IC where individual visionaries recognize that new ideas and technology can transform the world of intelligence. Many enterprising and energetic individuals expend herculean efforts to create new pathways, build out novel ideas, and find new solutions to old problems. They are willing to fail and accept the consequences because they believe so strongly in the importance of their work.

IC leaders must harness this grassroots energy and enthusiasm by actively supporting and connecting innovators, rewarding creativity even when a project fails, and empowering the workforce to effectively manage risk. The DNI should designate a senior leader to drive innovation across the IC, starting with a 90-day action plan that takes advantage of the AI crisis to kick-start activities that will help shape the IC culture toward one that embraces and supports innovation more broadly. Strong and steady leadership, clear prioritization, and a willingness to hold the organization accountable for achieving those goals are critical for creating and adapting a culture.

As with all organizations, some IC officers will embrace changes quickly. However, people with a long history in the IC often become attached to existing processes and mechanisms, mistaking them for the ethos of the IC. And because major culture change cannot leave all existing processes untouched, it can provoke skepticism and pushback from those folks. But dragging them along or leaving them behind is not an option officers with time and depth in the IC are critical; they make up the engine of the community, and they support and nurture employees throughout it. Actively engaging them as a part of the process and focusing on small wins that provide tangible benefit will help cultivate buy-in for larger, overarching goals. Proving out a few impactful ideas in pilot programs will show the IC can handle increased flexibility and speed without losing the security, rigor, and accountability required. This will, in turn, refresh the ICs culture while preserving and championing its strengths, and pave the way for innovation and AI adoption at scale.

Because no matter how many brilliant minds come together to create excellent recommendations to take advantage of AI and promote innovation, the IC will not successfully implement them without addressing the institutional resistance to new ways of doing business that acts as a self-sustaining barrier between the IC and widespread AI adoption. As Winston Churchill admonished, the IC must not let this crisis go to waste.

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Artificial Intelligence in the IC: Culture is Critical - The Cipher Brief

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The Role of Artificial Intelligence (AI) in the Global Agriculture Market 2021 - ResearchAndMarkets.com - Business Wire

Organizations that profit the most from Artificial Intelligence (AI) projects are more likely to believe in cognitive capabilities. Winning over the end-users of AI-enabled capabilities is just as crucial, if not more, than winning over the AI-enabled capabilities themselves.

CIOs and other IT leaders who want to scale their AI programs need to win support across the organization.

According to the 2020 Market Research Report from Fortune Business Insights, artificial intelligence had a market value of US$27.23 billion in 2019 and by 2027 this figure is expected to increase nearly tenfold in just eight years, with a Compound Annual Growth Rate (CAGR) of 33.2 percent.

Here are a few steps IT leaders can take to gain buy-in and sponsorship from C-suite executives and line-of-business colleagues:

Senior executives should contribute significantly to the success of the project. Executives should collaborate with their AI teams to verify that the AI systems input and output are consistent with the companys overall digital transformation strategy. Collaborative strategy sessions with executives and AI researchers can help raise awareness of Artificial Intelligence initiatives and keep AI research and development efforts focused on business objectives.

Also Read: 4 Strategies to Enable Asynchronous Collaboration in Hybrid Work

Its critical to connect and organize advisory councils with the organizations partners, suppliers, and employees when making changes like this to gather their ideas and perspectives on implementation. Businesses will be able to know where it is wanted and needed if they do so.

Businesses should understand that while IT can help with AI-enabled innovation, it has to be a collaborative effort. Organizations should remember to foster applied interest before deciding on their next course of action.

IT leaders and managers need to illustrate how AI will help employees in order to boost user adoption within a business. Present high-quality data that demonstrates how to enhance business operations. Incentivize usage by presenting a successful use case with senior leadership support and plainly recognizable statistics. IT executives and managers should properly communicate to team members why Artificial Intelligence is helpful, and the positive impact it will have on productivity and efficiency on a daily and long-term basis.

Maintaining a people-centered mindset will go a long way. Retaining staff that have the required skills and expertise working with AI systems can pay off.

Many functional leaders are afraid of losing their jobs or becoming outdated, which is one of the barriers to AI adoption. Line managers who dont completely understand the potential of AI and ML will be overwhelmed and become defensive about the human aspects of their employment if its positioned simply as a technical upgrade or cost-saving breakthrough.

Through spotlighting that AI enables teams to focus on the actions to take based on the insights generated by AI and ML solutions rather than spending time mining the data for patterns, it becomes apparent that AI does not eliminate the need for human decision-making, but rather facilitates a more effective and efficient path to accurate results.

Also Read: Three strategies for Maximizing SaaS Spend

Business leaders could be reluctant to accept Artificial Intelligence or machine learning models outputs seriously. Changing the corporate attitude requires instilling trust by engaging with business leaders to demystify AI solutions, how they function, and how the outcomes are generated.

When business leaders recognize the value of the outcomes provided by AI solutions, they are more likely to unearth disruptive insights and be willing to use them to drive desired business goals and have a major influence across the organization.

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Building Support and Addressing Concerns to Promote Artificial Intelligence in the Workplace - EnterpriseTalk