Monthly Archives: February 2022

Israeli predictive analytics platform, Pecan AI, has secured $66 million in a Series C funding round led by global private equity and venture capital firm, Insight Partners, along with support from prior investors including GV, S-Capital, GGV Capital, Dell Technologies Capital, and others.

This raises the companys total funds raised to over $100 million.

Pecan AI said the funding will be used to scale its global footprint and accelerate research and development of thelow-code predictive modeling and data science platform.

Founded in 2018, Pecans AI services aid business intelligence, operations, and revenue teams to predict revenue-impacting risks and outcomes without the need for data scientists. Pecan enables its users to transform substantial amounts of raw transactional data into accurate insights predicting the profitability impacts key performance indicators such as customer lifetime value, retention, conversion rates, and demand forecasting directly yield.

To date, Pecans predictive algorithms impact billions of dollars in revenue for consumer goods, fintech, insurance, mobile application, and wellness and beauty companies of various sizes, tripling its annual revenue from the past year.

We believe that any company should be able to deploy AI-based predictive analytics, even without data science resources on staff, said Zohar Bronfman, CEO and co-founder of Pecan AI. This new funding will help us scale Pecan further to overcome the data science scarcity gap, enabling our customers to move beyond outdated data-mining techniques that offer little value in predicting future outcomes.

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Pecan AI Raises $66M To Advance AI Automation And Predictive Analytics - NoCamels - Israeli Innovation News

Magnetic resonance imaging (MRI) technology is a widely used albeit costly tool for diagnosing brain injuries and strokes. Its high procurement, installation and operating costs, however, mean much of the developing world has no access to it.

Researchers from the University of Hong Kong (HKU) have successfully developed a new magnetic resonance imaging (MRI) technology, the ultra-low field (ULF) 0.055 Tesla brain MRI, which can operate from a standard AC wall power outlet and requires neither radiofrequency nor magnetic shielding room. Further, a conventional, typical MRI machine can cost up to US$3 million, yet the ULF-MRI scanner costs only a fraction of this price.

The research team was led by Professor Ed X. Wu, Chair of Biomedical Engineering and Lam Woo Professorship in Biomedical Engineering of the Department of Electrical and Electronic Engineering, HKU. The research output was published inNature Communications, and also highlighted inNature AsiaandScientific American.

The HKU team is one of the three leading ULF-MRI academic research groups worldwide, with one based at Harvard/MGH, dedicated to developing novel ULF-MRI technology. Their goal, as shared by researchers like Professor Wu, is to popularise and broaden the use of MRI.

As an MRI researcher for over 30 years, Professor Wu is delighted and derives a strong sense of fulfilment from the development of what he calls a scaled-down MRI scanner that is far more affordable than what is on offer in hospitals. The human body is mostly made of water molecules, on which MRI thrives, said Professor Wu. MRI is a gift from nature and we must use it more. Currently, it is underutilised as a diagnostic tool.

It is estimated that currently more than 90% of MRI scanners are located in high-income countries, and two-thirds of the worlds population do not have access to them. The total number of clinical scanners is estimated at only about 50,000 worldwide.

The HKU team has made the design and algorithms of ULF 0.055 Tesla brain MRI open-source knowledge, available to all interested in developing the technology further or applying it in diverse areas. This virtually opens the door to making advancements in various aspects of healthcare provision in terms of MRI applications. This will be a big field, Professor Wu said, the team has demonstrated the concept and shown the feasibility of a simplified version of MRI. There are many ways to move forward.

With the use of a deep learning algorithm, the team has removed the constraint in conventional MRI, namely the need to be shielded from the outside radiofrequency signal, which results in a bulky, non-mobile set-up. The existing MRI scanners are essentially giant magnets and need a purpose-built room to shield them from outside signals and to contain the powerful magnetic fields generated by their superconducting magnets, which require costly liquid helium cooling systems. The teams new computing and hardware concept made the latest development possible.

Professor Wu is confident that a critical mass of researchers could push the frontiers of knowledge. He noted that the open-source approach is the quickest way to spread knowledge. It is hoped that MRI can be used in more fields other than radiology, for example in paediatrics, neurosurgery or the emergency room. The team welcomes more people from the scientific, clinical and industrial sectors to research to benefit healthcare, he said.

In collaboration with Professor Gilberto Leung of Neurosurgery and other clinicians at Queen Mary Hospital, his team had validated the results of using ULF-MRI by comparing them with images obtained from a standard 3 Tesla MRI machine. They could identify most of the same pathologies, including stroke and tumours results, despite the lack of clarity and resolution required for precision diagnostics.

Professor Wu said, I believe computing and big data will be an integral as well as inevitable part of the future MRI technology. Given the inherent nature of MRI, I believe widely deployed MRI technologies will lead to immense opportunities in the future through data-driven MRI image formation and diagnosis in healthcare. This will lead to low-cost, effective, and more intelligent clinical MRI applications, ultimately benefiting more patients.

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Improving AI-enabled Healthcare in the U.S. - OpenGov Asia

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:

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

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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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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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