Daily Archives: February 7, 2022

Todays technological advancements have simplified our lives to the point where everything from email responses to social media postings can now be completely automated with the help of a scheduling software that leverages artificial intelligence.

Had the Covid-19 pandemic hit in the 1990s, businesses and peoples lives would have been more adversely affected, while the economic damage would have been astronomical.

However, todays innovative technologies have offset much of the pandemics damage to our economy. Over the past two years, for instance, I have led remote teams and initiated and executed multiple projects often without physically meeting my clients.

But even with all of these technological advances, one thing that AI will not be able to replace is our creativity; the compilation of our complex emotions, thoughts and aspirations.

AI has the ability to process information that is already out there. While it is good at replicating repetitive or predictive tasks, creativity is deeply rooted in the human experience and out of reach for AI.

The spark of inspiration that prompts artists or designers to think of a new future, a new solution or come up with an art piece that connects people across time and space, is something that AI cannot compete with.

Creativity is, without a doubt, the most coveted skill of the future and one that businesses and governments need to incorporate in their processes to stay ahead.

As an Arab society, creativity and creatives have always been held in high regard. Poetry, for example, had a special place in the Holy Kaaba during the pre-Islamic period.

Poems recited by the greatest Arab poets of the time, such as Antarah ibn Shaddad and Labid, have been written in gold on strips of Egyptian cotton and suspended from the interior walls of the Holy Kaaba. The creativity of poets was also celebrated annually at Souk Okaz, in Taif, Saudi Arabia, where Arabs would gather from faraway lands to recite poetry and sell their merchandise.

The appreciation of poetry and poets not just then, but even now is testimony to how we as a society have always valued creativity. We now see that passion and respect for creativity and creatives reflected in our national policies.

The Dubai Creative Economy Strategy, for instance, aims to double the contribution of the creative sector to the gross domestic product of Dubai to 5 per cent by 2025, from 2.6 per cent in 2020.

Meanwhile, the UAEs National Strategy for Cultural and Creative Industries (CCI) announced last December that it will introduce 40 initiatives across three segments in the creative sector: talents and creatives, professionals and business environment, and enabling of the business environment.

The strategy establishes a new phase in the future of the creative economy and lays down a strong foundation to enhance the contribution of the cultural and creative sector to achieve sustainable development, Noura Al Kaabi, Minister of Culture and Youth, said at the time.

The work of creatives inspires us and drives us to change, to dream and to build a better future. The technology race will not slow down. A year from now, the way we work may completely change. Technology is likely to be so entrenched in our lives that we may realise theres no area left where it does not dominate.

But creativity is what will help us stay ahead of the game. For that to continue, we need to incorporate creativity in our decision-making processes for it to play an active role in building the future and helping us come up with solutions to global issues.

Creativity will always be the distinguishing factor the one thing that AI will never be able to overcome.

Manar Al Hinai is an award-winning Emirati writer and communications consultant based in Abu Dhabi. Twitter: @manar_alhinai

Updated: February 7th 2022, 3:30 AM

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Why artificial intelligence will never replace creativity - The National

The latest research on Global Artificial Intelligence (AI) in Security Market Report 2021 offered by Adroit Market Research provides a comprehensive investigation into the geographical landscape, industry size along with the revenue estimation of the business. Additionally, the report also highlights the challenges impeding market growth and expansion strategies employed by leading companies in the Artificial Intelligence (AI) in Security market.

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Proceeding further, the business intelligence report of Market incorporates segmentation studies including product and application categories, and Regional-level analysis of the top geographies. Moving to the market competitive scenario, product and service offering of the prominent organizations along with business strategies employed by them to maintain a strong hold in this marketplace are reviewed thoroughly.

The statistical information presented in this report is predicated on the Artificial Intelligence (AI) in Security Market in Government marketplace primary, secondary investigation and study, and media release. This comprises data via a global group of expertise from Artificial Intelligence (AI) in Security Market in Government notable players to provide the latest information on the international Artificial Intelligence (AI) in Security Market in Government marketplace. Moving forward, segmentation analysis is obviously explained considering all the significant probabilities pertinent to Market in Government market conditions.

PESTLE Analysis of Global Artificial Intelligence (AI) in Security Market:

Political (Political policy and stability as well as trade, fiscal and taxation

policies)

Economical (Interest rates, employment or unemployment rates, raw material

costs and foreign exchange rates)

Social (Changing family demographics, education levels, cultural trends,

attitude changes and changes in lifestyles)

Technological (Changes in digital or mobile technology, automation, research

and development)

Legal (Employment legislation, consumer law, health and safety, international

as well as trade regulation and restrictions)

Environmental (Climate, recycling procedures, carbon footprint, waste

disposal and sustainability)

The market share (by revenue) for the public players will be based on the information available in the public domain, and for the private players, such information will be provided on best effort basis, which will entirely be based on primary interviews and latest developments of the companies

Global Artificial Intelligence (AI) in Security Market Competition by TOP Players are, Amazon.Com, Inc., Fortinet, Google (Alphabet Inc.), IBM Corporation, Intel Corporation, Micron Technology Inc., Nvidia Corporation, Palo Alto Networks Inc., Samsung Electronics Co., Ltd., Symantec. Acalvio Technologies, Inc., Cylance Inc., Darktrace, Securonix, Inc., Sift Science, Sparkcognition Inc., Threatmetrix Inc., Xilinx Inc.

On the basis of the end users, the Artificial Intelligence (AI) in Security Market market research focuses on the status and outlook for major applications, consumption (sales), market share and growth rate for each application, including By Application (Identity and Access Management, Unified Threat Management, Antivirus/Antimalware, Risk and Compliance Management, Fraud Detection, and others), Industry Vertical (BFSI, retail, IT & Telecommunication, Automotive & Transportation, Manufacturing, Government & Defense, and others)

The market report primarily will help you to realize and find out the most forbidding and upsetting driving powers of Artificial Intelligence (AI) in Security Market in marketplace with anticipating the consequences on the worldwide industry.

This Artificial Intelligence (AI) in Security Market report studies the top producers and consumers, focuses on product capacity, value, consumption, market share and growth opportunity in these key regions, covering

1. North America (United States, Canada and Mexico)

2. Europe (Germany, France, UK, Russia and Italy)

3. Asia-Pacific (China, Japan, Korea, India and Southeast Asia)

4. South America (Brazil, Argentina, Colombia etc.)

5. Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

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Artificial Intelligence (AI) in Security Market Expected to Witness a Sustainable Growth over 2026 | Amazon.Com, Inc., Fortinet, Google (Alphabet...

Early evidence suggests chatbots could play a role in reversing negative hiring trends and jump-starting the countrys economic recovery.

Ten years ago, Georgia State University was facing a challenge that may seem strange on its surface. A surprisingly large number of admitted students around 20 percent, all of whom had indicated their intention to enroll werent showing up for the first day of class.

This phenomenon, which has come to be known as summer melt, wasnt just an issue at Georgia State. Research suggests up to40 percentof students from low-income communities who are accepted to college never matriculate. For many of those students, the maze of paperwork and other requirements not to mention the fact that they are often the first in their families to attend college and may lack the sort of support so many college students take for granted puts an end to their educational aspirations, before they even arrive on campus. It may go without saying that the pandemic has only exacerbated the challenge.

But the story of summer melt is not limited to higher education. In the world of work, theresevidencethat so-called ghosting, in which workers who have accepted a job stop engaging with their employer before their first day, has been on the rise. One study conducted in 2020 found 28 percent of job seekers report ghosting an employer, up from just 18 percent the previous year. And that was before the Great Resignation, which has seen more workers quit their jobs in a concentrated period than ever before.

While the names may be different, the challenge is similar: too many people arent taking the next step in their personal journey, be it educational or professional. What are the barriers standing in their way and what can be done about them?

As it turns out, colleges and universities may have something to teach the business community. The same tools being used to address summer melt hold the potential to tackle ghosting in the workplace.

In 2016, Georgia State undertook a first-of-its-kind experiment: using artificial intelligence to help students navigate the transition to college. Accepted students were contacted via text message by a chatbot named Pounce (named for the schools mascot), which proactively reminded them about key deadlines and provided guidance to make sure they completed all the requisite tasks. The results speak for themselves. According to arandomized controlled trial, students who received the outreach were 3.3 percentage points more likely to enroll in the fall. That represents hundreds, if not thousands, of students who owe their educational experience to the support of an AI chatbot.

Since that study, many more colleges have followed in Georgia States footsteps, as have states likeWashingtonandTexas, and nonprofit organizations like theCommon App. Using a framework calledbehavioral intelligence, which brings together 24/7 support with personal and contextual understanding, these partnerships are demonstrating the potential of AI to help students take the next step in their journey to and through college.

With these promising results, it stands to reason the approach may work for entry-level workers much the same way it does for incoming college students. What would that strategy look like in practice?

Over the past year, a growing number of employers havebegun experimentingwith the application of AI and behavioral intelligence to address ghosting and help smooth the path into a new job. So far, the results are promising. While the projects are just in their pilot phase now, theyve demonstrated measurable decreases in ghosting and helped more early-career workers start their job on the right foot.

At a time when the so-called Great Resignation is upending the labor market as we know it, approaches such as these have never been more important as a way for employers to connect and communicate with incoming talent more effectively.

Just as importantly, AI is beginning to help employers listen at scale. Consider the results of one survey that a chatbot issued to jobseekers about why they dropped from the hiring process. For many respondents, logistics were the greatest challenge: one person missed an internet speed test and couldnt reschedule and another received word that their background check never came back. These seemingly small things made the difference between starting a new job and starting over from scratch. And both are the exact sort of challenge that AI has helped to address in the higher education context.

While theres still much more to learn, its clear AI can make a difference for people who need the right nudge, at the right time, to continue their progress, whether educational or professional. If these pilot programs in the workforce context prove effective, as they initially seem to be, how could chatbots play a role in reversing negative hiring trends and jump-starting the countrys economic recovery?

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Can AI solve ghosting in the workforce? - Talent Management

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Retailers need more decision automation, faster coordination of supply chains, and faster interactions with consumers, which means they will increasingly rely on AI. Automated decisioning systems will soon be making fine-grained micro-decisions on the retailers behalf, impacting customers, employees, partners, and suppliers. But these systems cant run autonomously they need human managers.

So what exactly should this human management look like?

Every system for making micro-decisions needs to be monitored. Monitoring ensures the decision-making is good enough while also creating the data needed to spot problems and systematically improve the decision-making over time.

Consider the following retail example: A fashion retailer that had historically applied blunt rules to determine markdowns decided to implement a new AI-powered solution. The system performed well for the first few weeks, making more frequent and more surgical decisions than human managers were able to contemplate. But at the start of the swimwear season, the system identified a slow initial sell-through that triggered all swimwear to be marked down to sell-out. As a result, the retailer lost millions of dollars of margin and was left with no swimwear.

Why did this happen? The aggressive markdowns were triggered because the first three weeks of sales were lower than expected. A human merchandiser would not have panicked and would have realized that this was due to a couple of particularly cold weeks. But the unmanaged and unmonitored AI system simply executed on its logic.

The example above illustrates why the best approach to deploying AI is typically semi-automation: automation that involves some level of human oversight. When optimized for each decision, semi-automation can help retailers save time, empower employees, and greatly improve profitability, while avoiding costly pitfalls.

The four models for semi-automation range from heavy to very light human involvement.

First, human in the loop (HITL) is the most basic framework for semi-automation, where decisions are rarely made without human involvement. Such a system provides recommendations based on automated calculations, but a human ultimately makes the decision. For example, pricing software calculates the ideal price of a dress to maximize profitability, but the pricing manager must sign off on each decision.

The next model is human in the loop for exceptions (HITLFE), where humans are removed from standard decision-making, but the system engages a manager when human judgment is required. For instance, if the automated system has two vendor options for stock replenishment, the buyer is required to step in and make the final call.

Then there is human on the loop (HOTL), which means the machine is assisted by a human. The machine makes the micro-decisions, but the human reviews the decision outcomes and can adjust rules and parameters for future decisions. In a more advanced setup, the machine also recommends parameters or rule changes that are then approved by a human.

Finally, there is human out of the loop (HOOTL), which is where a human simply monitors the machine. The machine makes every decision, and the human intervenes only by setting new constraints and objectives.

Selecting the right model to use is a design problem. As we have seen, automation is not all or nothing, and decisions are not created equal. The right model should be determined based on the decisions complexity, volume, velocity, and blast radius, which measures the potential downside.For example, if the decision is simply to recommend a blue dress instead of a red one because blue is out of stock, its a low-risk decision that can be fully automated with limited oversight. However, if the worst outcome results in misordering thousands of dresses or in expensive markdowns like in the swimwear example, then human oversight and accountability is more critical. Its also important to recognize that automated systems can and will evolve over time, enabled by new technology, the desire to make ever more fine-grained decisions, and managements confidence in automating business operations.

The key to the successful deployment of any AI system is to start with a quantified business problem. With this, retailers must foster a data-driven culture where the whole team is engaged in determining how best to improve specific business decisions. This also necessitates a change in how retailers do their jobs. For example, merchandising managers in the past might have had to set prices for several dozen dresses a day based on stock, sales data, and competitor activity. But now, with personalized promotions and recommendations, the same manager might be responsible for millions of decisions a day. This requires a fundamental shift from making decisions to making decisions about decisions i.e., managing rules and parameters rather than making specific pricing decisions.

Semi-automation of business-critical decisions must be approached carefully, with regard to the potential heightening of the blast radius of risk. Once the decision to automate has been made, retailers must shift their attention to decision algorithms the logic and rules that enable retailers to execute on the micro-decisions. Miscommunication between the data science team and the rest of the organization can lead to errors and missed opportunities, potentially creating a reluctance to change that can be quite difficult to reverse.

Whichever model you adopt, its critical to put AI on the organization chart to ensure that human managers feel responsible for its output. To succeed, retailers must understand the different ways they can interact with AI and pick the right management option for each AI system. Selecting the best level of semi-automation will ensure that the retail businesses realize the full potential of AI.

Michael Ross is Senior Vice President of retail data science at EDITED. He is a non-executive director at Sainsburys Bank and N Brown Group plc. He also cofounded several companies, including DynamicAction, ecommera, and figleaves.com. Prior to that, he was a consultant at McKinsey and Company.

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AI in retail has to be semi-automated. Heres why - VentureBeat

Global Wearable AI Market Overview:

Global Wearable AI Market presents insights on the current and future industry trends, enabling the readers to identify the products and services, hence driving the revenue growth and profitability. The research report provides a detailed analysis of all the major factors impacting the market on a global and regional scale, including drivers, constraints, threats, challenges, opportunities, and industry-specific trends. Further, the report cites global certainties and endorsements along with downstream and upstream analysis of leading players. The research report comes up with the base year 2021 and the forecast between 2022 and 2028.

This report covers all the recent development and changes recorded during the COVID-19 outbreak.

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Top Key Players in the Wearable AI Market:Apple, Samsung, Google, Microsoft, Sony, Garmin, Fitbit, Huawei, Amazon, IBM, Oracle

The Wearable AI Industry is severely competitive and fragmented due to the existence of various established players taking part in different marketing strategies to increase their market share. The vendors operating in the market are profiled based on price, quality, brand, product differentiation, and product portfolio. The vendors are turning their focus increasingly on product customization through customer interaction.

Major Types of Wearable AI covered are:Smart WatchEar WearEye Wear

Major end-user applications for Wearable AI market:Consumer ElectronicsEnterpriseHealthcare

Regional Analysis For Wearable AIMarket

North America(the United States, Canada, and Mexico)Europe(Germany, France, UK, Russia, and Italy)Asia-Pacific(China, Japan, Korea, India, and Southeast Asia)South America(Brazil, Argentina, Colombia, etc.)The Middle East and Africa(Saudi Arabia, UAE, Egypt, Nigeria, and South Africa)

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Wearable AI Market 2022 is projected to grow at a healthy CAGR Cleveland Sports Zone - Cleveland Sports Zone

[126 Pages Report] AI in ICT (Information and Communications Technology) Market Insights 2022 The main purpose of AI in Information and Communications Technology is to process and passinformation safely and accuratly since the information is vulnerable.

Market Analysis and Insights: Global AI in ICT (Information and Communications Technology) Market

In 2021, the global AI in ICT (Information and Communications Technology) market size will be USD million and it is expected to reach USD million by the end of 2027, with a CAGR of % during 2021-2027.

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On the whole, the report proves to be an effective tool that players can use to gain a competitive edge over their competitors and ensure lasting success in the global AI in ICT (Information and Communications Technology) market. All of the findings, data, and information provided in the report are validated and revalidated with the help of trustworthy sources. The analysts who have authored the report took a unique and industry-best research and analysis approach for an in-depth study of the global AI in ICT (Information and Communications Technology) market.

Global AI in ICT (Information and Communications Technology) Scope and Market Size

AI in ICT (Information and Communications Technology) market is segmented by company, region (country), by Type, and by Application. Players, stakeholders, and other participants in the global AI in ICT (Information and Communications Technology) market will be able to gain the upper hand as they use the report as a powerful resource. The segmental analysis focuses on revenue and forecast by Type and by Application in terms of revenue and forecast for the period 2016-2027.

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Leading key players of AI in ICT (Information and Communications Technology) Market are

AI in ICT (Information and Communications Technology) Market Type Segment Analysis (Market size available for years 2022-2027, Consumption Volume, Average Price, Revenue, Market Share and Trend 2015-2027): Software, Services

Regions that are expected to dominate the AI in ICT (Information and Communications Technology) market are North America, Europe, Asia-Pacific, South America, Middle East and Africa and others

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The announcement of Kisan Drones in the Union budget 2022 to promote drone technology for crop assessment, digitisation of land records, spraying of insecticides and nutrients, is poised to give a major fillip to drone startups. The finance minister said startups would be encouraged to facilitate Drone Shakti through varied applications and Drone-As-A-Service (DrAAS). In addition, the government will start required courses for skilling in select ITIs.

Reacting to the announcement in the budget, Athishay Jain, Co-founder and COO, ISPARGO, said adopting cutting-edge technology will increase farmers income. Using drones in farms is a low-risk proposition, definitely a great move from the government. If drone technology is adopted on a large scale which would increase the demand, the product will get better, and the ecosystem would naturally develop, he added.

Mangaluru-based robotic startup ISPARGO uses drones to apply insecticide on areca nut trees. Jain said the startup uses ultra-lightweight, compact power conversion technology to power the drones from the ground from any AC source.

AI/ML in drone

AI/ML models are used to develop drones capable of landing on a fixed image or a marker on the ground. The same solution is being extended to follow a moving vehicle. This complex AI/ML model captures the images, processes them, and predicts the next set of navigation points for the drone to follow, all within milliseconds.

ISPARGO, established in 2017, focuses on developing and customising drones for work.

Batteries prominently power drones, but they do not last beyond 30 minutes. Any long endurance work using drones will need 15 to 20 sets of batteries which is economically unviable. To upgrade the drone technology, ISPAGRO has developed non-battery powered drones which can work for a whole day with improved efficiency. They are also looking into customising the firmware for specific use cases like painting or pesticide spray to make operations almost autonomous.

The unique proposition of ISPARGO is the tethered drones used for surveillance, plantation crop spray and inspection/monitoring. The salient features of these drones are:

Use cases

For border surveillance/intrusion detection: Tethered drones solution comes with quick and easy deployment; secured data transmission with OFC; no cabling issues; supports 24 hours of uninterrupted video feed; thermal EO/IR, RGB cameras.

For temporary mobile towers: Tethered drone solution supports all-terrain operation; easy plus and fly operation on the go; easily transportable for quick fix deployment; requires minimum resources for installation; can be used for communication and surveillance for intrusion detection.

For agriculture/pesticide spray: Tethered drone solution has one skilled pilot who can cover more trees in a short time; precise spray, low wastage; timely and effective spray increase productivity; onboard camera aids in crop health analysis.

For windmill inspection: Tethered drone solution has quickly deployable precise inspection; no operator insurance and training cost; can fit sophisticated camera for stress/heat analysis.

Challenges

Solutions

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How this robotics startup uses AI to power its tethered drones - Analytics India Magazine

Sometimes, youre in the mood for a melodramatic story about ill-fated lovers thatll keep your heart rate high and your eyes wide open. Other times, youd rather watch a rom-com with stakes so low that you know everyones headed straight for happily-ever-after.

To find out which films best fit each mood, search marketing agency Honch used Rotten Tomatoes to compile a list of romances, and then fed all the screenplays through a sentiment analysis tool called TensiStrength. The program processed the language and determined how stressful or relaxing it was overall.

The results were interesting, to say the least. Topping the list of most stressful movies was 2011s 50/50, in which a 20-something-year-old (played by Joseph Gordon-Levitt) battles aggressive cancer and falls for his therapist (Anna Kendrick). You can understand how a computer system would construe that as a stressful watch, though it didnt exactly account for all the comic relief.

An inability to detect comic relief seems to be a trend. Behind 50/50 comes The Princess Bride (1987), which, while admittedly containing plenty of stressful elementsattempted murder, actual murder, poison, quicksand, torture, a Fire Swamp teeming with Rodents of Unusual Size, etc.is widely considered a delightful, kid-friendly fairytale. Other relatively light fare on the most stressful list includes 1999s Notting Hill, 1986s Ferris Buellers Day Off, and 1998s Shakespeare in Love.

What the least stressful list lacks in cancer and quicksand, it makes up for in interpersonal drama, loneliness, and character-driven commentary on mental health. Witnessing George Clooneys Ryan Bingham systematically lay off employees in Up in the Air (2009)which came in first placemay not seem too anxiety-inducing on paper, but the effect in the film itself isnt exactly relaxing. Similar things could be said about 2002s Punch-Drunk Love (in fifth place) and 2012s Silver Linings Playbook (ninth). But wehumans and machines alikecan all agree that 1995's Clueless, number 15, is a very fun flick.

While the lists below might not be a perfect watch guide, theyre a good reminder of just how much goes into making a movie beyond the screenplay. You can also take heart in the notion that human intelligence may still have an edge over artificial intelligence, at least when it comes to watching movies.

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The 20 MostAnd 20 LeastStressful Romance Movies, According to AI - Mental Floss

WASHINGTON The Pentagons chief information officer will also serve as the head of a new organization overseeing the Defense Departments various digital and artificial intelligence efforts, the department announced Feb. 2.

DoD Chief Information Officer John Sherman will serve as the acting chief digital and artificial intelligence officer, or CDAO, a newly created office designed to oversee the Defense Digital Service, the Joint Artificial Intelligence Center and the CIO office he was already leading. The new office was established to better align a number of data, analytics, digital solutions and AI efforts across the DoD. Previously, all three of those offices reported directly to the deputy defense secretary.

Sherman will serve as DoD CIO and CDAO as the Pentagon continues to look for a director.

Im honored to be able to help get this organization stood up, again, while performing my chief information officer duties and also serving as the acting CDAO, Sherman said. In addition to getting CDAO up and ready for [full operational capability], rest assured well remain laser focused on our CIO duties of cybersecurity, digital modernization, C3 [command, control and communication], and other areas that the department relies on.

Sherman has a long history working on national security and information issues. After serving a three-year stint as the chief information officer of the intelligence community, Sherman joined the DoD as the principal deputy chief information officer. Just over a year ago, he was named acting chief information officer by the incoming Biden administration.

The CDAO was established Dec. 8 and achieved initial operating capability Feb. 1, the Pentagon noted. Full operating capability is expected by June 1, and the Pentagon wants a leader selected for the position by then. Once that is accomplished, the Pentagon will submit proposals to Congress to adjust authorities and reporting lines accordingly. The department expects about 200-300 people to be combined under the new office with an approximately $500 million budget.

The Pentagon also issued a memo clarifying how the new CDAO position differs from other high-level positions within the Office of the Secretary of Defense.

For example, the undersecretary of defense for research and engineering will lead data, analytics and Al policy related to basic research through prototyping, while the CDAO will carry those efforts from prototyping to operations. The CIO will continue to lead on core infrastructure such as cybersecurity, cloud, data transport and networks while the CDAO will set requirements for that core infrastructure and provide policy and guidance for the data, analytics and AI that interact with it.

Nathan Strout is the staff editor at C4ISRNET where he covers the intelligence community.

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Pentagon names acting chief digital and AI officer as it moves toward full capability - C4ISRNet