Category Archives: Artificial Intelligence

If I were to ask someone why they chose a career in information technology, I doubt they would respond withI love data entry!,I could debug code all day long!, orHandling tickets is so much fun, Id do it even if I didnt get paid for it.

Fortunately, AI can help. Here are the top three ways AI can help automate manual IT tasks, thereby freeing up precious resources and benefiting your teams, businesses, and customers.

Grace Murray Hopper was a Navy rear admiral and computer programming pioneer who worked on the Mark II computer at Harvard in the 1940s. On September 9, 1947, Hopper traced an error with the Mark II to of all things a dead moth in the relay. The insects remains were taped in the teams logbook with the caption, First actual case of a bug being found.

While Hopper and her team werent the first to use the term bug to describe a system glitch, they certainly helped popularize it. Of course, software bugs are decidedly unpopular. IT departments and software engineers have all felt the pain of toiling over lines of code trying to reproduce and locate problems.

[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders:Cheat sheet: AI glossary. ]

To be as good as human engineers, an AI tool would need to possess levels of reasoning and creativity it simply hasnt yet reached. But AI can still be tremendously effective in exception and anomaly detection. You train it on normal usage and it detects when something is off.

Another advantage AI has over humans is its pattern detection. Lets say a system is crashing at the same time every week or after memory usage hits a certain level. An AI tool could easily connect the dots. AI can learn which behaviors of your developers and which code patterns that are checked into your repo are correlated to bugs. This can be used to notify developers that they have done something that is likely to break and ask them to check again.

If you had a moth infestation in your home, you could certainly go around swatting them one by one. But wouldnt it be a lot easier to discover where they hide and put out traps?

The adage an ounce of prevention is worth a pound of cure is as true in IT as it is in medicine. Monitoring operations and taking proactive action instead of just reacting to problems as they arise can prevent unexpected downtime and expensive failures.

CIOs and IT professionals are familiar with the value of preventative maintenance to some degree, whether its installing software updates or creating backups. That kind of maintenance is done after a certain amount of time has elapsed or usage has been logged. Its like eating vegetables or getting exercise theyre sound practices for a company.

[ Read also:4 Robotic Process Automation (RPA) trends to watch in 2022.]

Predictive maintenance, on the other hand, is individualized and custom-tailored. It monitors the equipment and its environment, performs tests, and receives equipment feedback to generate individualized predictions. Its like having a blood test show that youre pre-diabetic and in response, you design a low-sugar diet.

People may be uncomfortable with the idea of machines watching them all day. But with AI-enabled predictive maintenance, you watch the machines with other machines.

Dealing with IT tickets can feel like playing a perpetual game of Whack-A-Mole, but with all of the exhaustion and none of the fun carnival music and prizes.

Dealing with IT tickets can feel like playing a perpetual game of Whack-A-Mole, but with all of the exhaustion and none of the fun carnival music and prizes.

As we all know, some incidents are worth your attention and others arent at all. And without a proper way to triage incidents, IT departments become overwhelmed. Enter intelligent filters. Theyve been around for years in search engines and email inboxes, distinguishing between good and bad, important and unimportant. For IT departments, they can distinguish between real incidents and noise.

More on artificial intelligence

Using AI techniques like case-based reasoning can help decide which solution to explore first or what additional information to request from a customer to make a diagnosis quickly and accurately. Case-based reasoning systems learn from success and failure, apply sophisticated probabilistic reasoning to identify promising solutions, and create a valuable knowledge base.

With intelligent filters and case-based reasoning, IT managers can better allocate resources for incidents that require human intervention.

While there are numerous existing AI applications that help IT departments and many more yet to be discovered debugging, predictive maintenance, and intelligent filtering are three applications of AI that are essential for any great IT department today.

As AI becomes increasingly integrated into our work, any organization that is not actively exploring automating its more manual IT tasks is wasting valuable financial and human capital and may eventually fall behind.

[ How does AI connect tohybrid cloud strategy? Get the free eBooks,Hybrid Cloud Strategy for DummiesandMulti-Cloud Portability for Dummies. ]

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Artificial intelligence (AI): 3 everyday IT tasks where automation fits - The Enterprisers Project

[90 Pages Report] Artificial Intelligence & Advanced Machine Learning Market Insights 2022 This report contains market size and forecasts of Artificial Intelligence & Advanced Machine Learning in China, including the following market information:

China Artificial Intelligence & Advanced Machine Learning Market Revenue, 2016-2021, 2022-2027, (USD millions)

China top five Artificial Intelligence & Advanced Machine Learning companies in 2020 (%)

The global Artificial Intelligence & Advanced Machine Learning market size is expected to growth from USD million in 2020 to USD million by 2027; it is expected to grow at a CAGR of % during 2021-2027.

The China Artificial Intelligence & Advanced Machine Learning market was valued at USD million in 2020 and is projected to reach USD million by 2027, at a CAGR of % during the forecast period.

The Research has surveyed the Artificial Intelligence & Advanced Machine Learning Companies and industry experts on this industry, involving the revenue, demand, product type, recent developments and plans, industry trends, drivers, challenges, obstacles, and potential risks.

Get a Sample PDF of report https://www.360researchreports.com/enquiry/request-sample/19613829

Leading key players of Artificial Intelligence & Advanced Machine Learning Market are

Artificial Intelligence & Advanced Machine Learning Market Type Segment Analysis (Market size available for years 2022-2027, Consumption Volume, Average Price, Revenue, Market Share and Trend 2015-2027): Smart Wallets, Voice-Assisted Banking

Regions that are expected to dominate the Artificial Intelligence & Advanced Machine Learning market are North America, Europe, Asia-Pacific, South America, Middle East and Africa and others

If you have any question on this report or if you are looking for any specific Segment, Application, Region or any other custom requirements, then Connect with an expert for customization of Report.

Get a Sample PDF of report https://www.360researchreports.com/enquiry/request-sample/19613829

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(New Report) Artificial Intelligence & Advanced Machine Learning Market In 2022 : The Increasing use in Insurance, Banking and Capital Markets is...

SUNNYVALE, Calif.--(BUSINESS WIRE)--Celestial AI, an AI-accelerator company with a proprietary hardware and software platform for machine learning chipsets, today announced a $56 million Series A investment led by Koch Disruptive Technologies (KDT) with participation from Temaseks Xora Innovation fund, The Engine, the venture firm spun out of MIT, Tyche Partners, Mercks corporate venture fund, M-Ventures, IMEC XPand, and venture capital investor in the Princeton University ecosystem, Fitz Gate. The new capital will be used for expanding the global engineering team, product development and strategic supplier engagements, including Broadcom, to build the companys Orion AI accelerator products. Celestial AIs mission is to fundamentally transform the way computing is done with a new processing system, based on their proprietary Photonic Fabric technology platform, that uses light for data movement both within chip and between chips.

Driven by advancements in data communications, robust silicon photonics technology and volume manufacturing ecosystems have been established. The industry is ripe for commercial implementation of Machine Learning (ML) and high-performance computing (HPC) solutions that leverage integrated silicon photonics for data movement. For AI computing applications, data movement is the dominant contributor to system power, and most leading competitive architectures are trading off moderate power reductions for increased system and software complexity. Celestial AIs Photonic Fabric enables optically addressable memory and compute (within chip and chip-to-chip) that decouples their technology from the limitations of electronics and slowdown of Moores Law. Their proprietary architecture enables elegant, low-complexity system software, allowing highly efficient mapping of data and compute without the need for complex optimizations. This software advantage extends to multi-chip exascale systems as the Photonic Fabric democratizes optical access to effectively limitless memory and compute. Celestial AIs Orion AI accelerator products serve an addressable market that is projected by Omida to exceed $70 billion in 2025.

We are addressing the problem of our time in computing efficient data movement, said Celestial AI founder and CEO David Lazovsky. Celestial AIs hybrid photonic-electronic platform allows us to leverage the complementary strengths of electronics for high-performance, high-precision computing and photonics for high-speed, low-power, high-bandwidth data movement. The result is transformational performance advantages relative to electronic-only systems. The ML application benefits extend beyond performance and low power to latency, user friendly software, and low total cost of ownership. Our competitive differentiation will increase with time, as AI model complexity increases, driving increased data movement.

Domain-specific architectures targeted to AI workloads can make up for some of the slowdown in CMOS advancements, but that approach also has its limits. By integrating photonics into accelerators for AI workloads, Celestial AI enables step-change advancements in AI computation. Chips and server systems are limited today by power budget (Thermal Design Power or TDP). The Celestial AI Photonic Fabric allows a redistribution of the fixed power budget from data movement to compute, providing sustainable and expanding performance advantages over all electronic-only solutions. Every Joule of energy saved on data movement can be spent on compute.

Photonics is poised to be the technology to usher in the next era of rapid growth in AI and high-performance computing, and we believe the Celestial AI team has the experience and vision to drive this industry transformation, said Isaac Sigron, Managing Director of KDT, and newly-appointed Celestial AI Board Member. It was Celestial AIs software advantages that ultimately drove our decision to lead this financing. Their system architecture enables unparalleled software simplicity, which translates to ease of use for customers and reduced time to market. Software is the pathway to revenue, and Celestial AIs solution changes the game in this large and rapidly expanding market.

Celestial AI has developed an architecture that uniquely scales across multi-chip systems, and greatly diminishes the development burden on AI teams bringing new applications to market. As the world moves to increasingly complex AI models, we believe that Celestial AIs competitive advantage will only grow over time, said Phil Inagaki, Managing Director at Xora Innovation.

ABOUT CELESTIAL AI

Celestial AI is an AI accelerator company with a proprietary hardware and software technology platform which enables the next generation of high-performance computing solutions. Celestial AIs mission is to fundamentally transform the way computing is done with their proprietary Photonic Fabric technology that uses light for data movement both within chip and between chips.

ABOUT KOCH DISRUPTIVE TECHNOLOGIES

Koch Disruptive Technologies (KDT) is a unique investment firm, focused on empowering founders to create a could-be world. KDT provides a flexible, multi-stage investment approach which includes both traditional venture and growth stages. We work with principled entrepreneurs who are building transformative companies, disrupting the status quo, and creating new platforms. KDT is a subsidiary of Koch Industries, one of the largest privately held companies in the world with $115 billion in revenue and operating in more than 70 countries. KDT helps its partners unlock their full potential by bringing Kochs capabilities and network to them, structuring unique capital solutions, and embracing a long-term, mutual benefit mindset. For more information, visit http://www.kochdisrupt.com.

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Celestial AI Raises $56 Million Series A to Disrupt the Artificial Intelligence Chipset Industry with Novel Photonic-Electronic Technology Platform -...

A web-based cohort study suggests that, if machine learning algorithms are provided with a sufficiently wide range of predictive data, they can be induced to analyse epidemiologic data and predict the probability of conception with a discrimination accuracy which exceeds earlier studies.

One focus for AI research has been in predicting the chance of pregnancy - with varying success. A study last year found an AI-based model outperformed clinicians in assessing embryo viability, while a poster from last years annual meeting of preliminary research into predicting embryo ploidy showed that the algorithm tended to classify embryos as aneuploid.(1,2)

Adding to this evidence base, a new large prospective study has now found that algorithms are able to forecast the probability of conception among couples trying to get pregnant if given a wide range of data on predictors of fecundability (defined as the per-cycle probability of conception).(3) Based on a study participation cohort of more than 4000 women, results showed an overall discrimination performance of around 70% for six different supervised machine-learning algorithms in distinguishing between women who were likely to conceive and those who were not.

It was an outcome which, the authors say, exceeds results from predictive models in previous studies and demonstrates that such models can be created with reasonable discrimination using self-reported data. They add that this is in the absence of more detailed medical information such as laboratory or imaging tests.

Earlier work in this area has focused primarily on identifying individual risk factors for infertility. Several predictive models have been developed in sub-fertile populations but with limited power and using little or no data on lifestyle, environmental and sociodemographic factors. In contrast, a total of 163 predictors of fecundability were considered in this new study to anticipate the cumulative likelihood of pregnancy over six and 12 menstrual cycles.

The data were based on 4133 women from the ongoing Pregnancy Study Online (PRESTO), a web-based preconception cohort study which is analysing the impact of environmental and behavioural factors on fertility and pregnancy. Participants in the study were aged 2144 years, from the US or Canada, were not using fertility treatment, reported no more than one menstrual cycle of pregnancy attempt at study entry, and were actively trying to conceive at enrolment (20132019).

The female patients completed extensive questionnaires at enrolment (eg, marital status, reproductive and diet history, male partner characteristics, etc). Some of this information (eg, menstrual cycle dates) was updated via follow-up questionnaires completed bimonthly for 12 months, or until conception/cessation of pregnancy attempts or study withdrawal.

Next, the data were used to develop models to predict the probability of pregnancy. These were based on three time periods: pregnancy in fewer than 12 menstrual cycles (model I, n = 3195); pregnancy within six menstrual cycles (model II, n = 3476); and the average probability of pregnancy per menstrual cycle (model III, n = 4133). Additional models were also developed for women (n = 1957) who had never been pregnant but had no history of infertility: pregnancy in fewer than 12 menstrual cycles (model IV); pregnancy within six menstrual cycles (model V); and predicting fecundability (model VI). Six different supervised machine learning algorithms were then applied to each model to establish how each algorithm performed.

Results showed 86% of women in model I became pregnant and 69% in model II within the timeframes. For all six algorithms, the AUC (for prediction accuracy) was as follows: model I 68-70% (SD: 0.8%-1.9%); model II 65-66% (SD: 1.9%-2.6%); model III (63%); model IV 69.5% (SD: 1.4%); model V 65.6% (SD: 2.9); and model VI 60.2% concordant index.

Female age, female BMI and history of infertility were the predictors inversely associated with pregnancy in all models. The predictors positively associated with pregnancy in the first three models were having previously breastfed an infant and using multivitamins or folic acid supplements. Among the nulligravid women, the most important predictors were female age, female BMI, male BMI, use of a fertility app, attempt time at study entry and perceived stress.

The authors conclude that the findings are especially relevant for couples planning a pregnancy and for clinicians caring for women coming off contraception to have a baby. However, they add that the models do need to be validated in external populations before they can become a counselling tool.

1. VerMilyea M, Hall J, Diakiw S, at al. Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF. Human doi: 10.1093/humrep/deaa0132. Aparicio Ruiz B, Bori L, Paya E, et al. Applying artificial intelligence for ploidy prediction: The concentration of IL-6 in spent culture medium, blastocyst morphological grade and embryo morphokinetics as variables under consideration. Human Reprod 2021; doi.org/10.1093/humrep/deab127.0663. Yland J, Wang T, Zad Z, et al. Predictive models of pregnancy based on data from a preconception cohort study. Human Reprod 2022; 1-13; doi.org/10.1093/humrep/deab280

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The algorithm will see you now: artificial intelligence in the prediction of pregnancy - ESHRE

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Artificial intelligence goes far beyond just music or clothing recommendations which poses unforeseen risks for all of us. In his new book The Loop, NBC News Technology correspondent Jacob Ward warns AI is eroding our ability to make decisions on our own. He tells Ali Velshi that companies are deploying these pattern recognition systems to figure out what you and I are going to do nextthe capacity for manipulation and even predatory tactics is enormous. He adds AI offers unscrupulous businesses the opportunity to make incredible money off us by just playing to our worst instincts.Jan. 30, 2022

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Your Brain on AI: Artificial Intelligence is creating a world without choices - MSNBC

The Ministry of Agriculture and Forestry plans to implement artificial intelligence (AI) technology to tackle forest fires, which destroyed large swaths of land last year.

AI will be used in the Remote Smoke Detection-Early Fire Warning System developed by the ministry. It will enable a faster response to fires. Forestry Minister Bekir Pakdemirli said the technology will be used in cameras set atop watchtowers in the forests. In an interview published by Yeni afak newspaper on Wednesday, he stated that cameras can detect smoke from a distance up to 20 kilometers (12.4 miles) through smoke perception, and the new system would reduce the detection time to two minutes.

The system is currently installed in Antalya and Mula, two Mediterranean provinces that lost hundreds of acres of forests to devastating wildfires in the summer of 2021, one of the worst and deadliest outbreaks in the region. AI enables us to keep track of the smoke and deploy our teams as soon as possible, Pakdemirli said.

The ministry has 76 smart watchtowers, entirely operated without staff and 103 towers installed with cameras. Cameras, through AI and machine learning, are able to send alarm signals to authorities, via text or multimedia message, upon detection of smoke. Every tower can scan an area of up to 50,000 hectares in two minutes and can send exact coordinates of the fire.

Forest fires, worsened by the ongoing climate crisis, are a major concern for Turkey, which has expanded its forest cover in the past two decades. President Recep Tayyip Erdoan said on Monday after a Cabinet meeting that they were working to boost infrastructure to fight forest fires. We will increase the number of domestically manufactured unmanned aerial vehicles (UAVs) to eight, the number of firefighting planes to 20 and helicopters to 55, Erdoan said.

Turkey suffered from at least 2,105 forest fires last year, though the worst was in Antalya and Mula. Strong winds and extreme temperatures hampered efforts to douse the fires. The country witnessed an unprecedented surge in forest fires starting from the last week of July, a period with the highest number of almost simultaneous forest fires. It took around two weeks for authorities to put out all 240 wildfires that had raged across the country forcing the evacuation of hundreds of people.

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Turkey taps artificial intelligence in its fight against wildfires | Daily Sabah - Daily Sabah

This new connection between the institutions will facilitate the sharing of co-advised thesis or participating on the dissertation committee for students & PhD candidates and the interchange of scholarly papers, research materials, and other information in both parties areas of interest. This cooperation involves collaborative research and activities and strong internship chances at AIISC for IIIT-Delhi students. The MoU further specifies that the parties can develop specific joint educational programmes in the future and enjoy the benefits of interchange of research, teaching, and technical personnel.

The Center for Artificial Intelligence (CAI), IIIT-Delhi and AIISC have many knowledge and skills from world-class academic experts to students. This Memorandum of Understanding will focus on productivity and a desire to bridge the knowledge gap and promote innovation. This association will provide ground breaking results that will benefit all the parties involved.

Artificial Intelligence Institute, University of South Carolina (AIISC) aspires to be a leader in Artificial Intelligence (AI) and its applications. It fosters comprehensive multidisciplinary & translational AI research across the institution, workforce and economic growth in the state through education, technology, and commercialisation, in addition to many primary research topics in AI.

Prof. Amit Sheth, Director, AIISC, commented, "Since I visited IIITD a decade ago, I have seen it build one of the best research ecosystems among Indian universities. AIISC, a university-wide institute at the state flagship, Carnegie R1, University of South Carolina, already has over 30 researchers, strong foundational research in AI complemented by equally strong translational research. I look forward to having CAI/IIITD students among the AIISC's large pool of remote and on-site interns working on world-class research, with access to faculty from both organizations and having access to our exceptional computing resources. The research collaborations will result in excellent publications and add to the eminence of both organizations.

The Centre for Artificial Intelligence (CAI) aspires to be India's primary AI development centre. It comprises basic AI algorithms for furthering research and AI applications for tackling societal problems in the Indian context.

"I firmly believe that this MOU will open up huge opportunities for joint collaboration in terms of not only research but also several academic activities, exchange programs, and so on, stated Dr. Tanmay Chakraborty, Head, CAI, IIIT-Delhi, in response to the collaboration. He added, "AIISC, a recent university-wide institute at the University of South Carolina founded in 1801, has grown massively in the last few years. I, myself, have witnessed the growth. The Center for AI at IIITD (CAI) is also one of the old AI centres in India established by the generous funding of Infosys Foundation with the goal of advancing AI-related Interdisciplinary research. Both the institutions have unique skillsets and would bring in complementary expertise. I am super excited to witness the success of this collaboration."

Indraprastha Institute of Information Technology, Delhi (IIIT-Delhi) has a strong engineering background and connections to researchers and medical professionals from several Indian universities, including AIIMS and others. The Delhi Government established IIIT-Delhi as a state university in 2008, allowing it to conduct research and award academic degrees. IIIT-Delhi has risen to become one of India's most promising new institutions, with world-class professors and an atmosphere that strives to encourage state-of-the-art research and innovation while enabling entrepreneurial activities that bring deep-tech benefits to society.

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Center for AI at IIIT-Delhi and Artificial Intelligence Institute, University of South Carolina Sign MoU to Set Academic Cooperation and Research...

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence in Supply Chain Management Market by Technology, Processes, Solutions, Management Function (Automation, Planning and Logistics, Inventory, Risk), Deployment Model, Business Type and Industry Verticals 2022 - 2027" report has been added to ResearchAndMarkets.com's offering.

This report provides detailed analysis and forecasts for AI in SCM by solution (Platforms, Software, and AI as a Service), solution components (Hardware, Software, Services), management function (Automation, Planning and Logistics, Inventory Management, Fleet Management, Freight Brokerage, Risk Management, and Dispute Resolution), AI technologies (Cognitive Computing, Computer Vision, Context-aware Computing, Natural Language Processing, and Machine Learning), and industry verticals (Aerospace, Automotive, Consumer Goods, Healthcare, Manufacturing, and others).

This is the broadest and most detailed report of its type, providing analysis across a wide range of go-to-operational process considerations, such as the need for identity management and real-time location tracking, and market deployment considerations, such as AI type, technologies, platforms, connectivity, IoT integration, and deployment model including AI-as-a-Service (AIaaS).

Each aspect evaluated includes forecasts from 2022 to 2027 such as AIaaS by revenue in China. It provides an analysis of AI in SCM globally, regionally, and by country including the top ten countries per region by market share.

The report also provides an analysis of leading companies and solutions that are leveraging AI in their supply chains and those they manage on behalf of others, with an evaluation of key strengths and weaknesses of these solutions.

It assesses AI in SCM by industry vertical and application such as material movement tracking and drug supply management in manufacturing and healthcare respectively. The report also provides a view into the future of AI in SCM including analysis of performance improvements such as optimization of revenues, supply chain satisfaction, and cost reduction.

Select Report Findings

Modern supply chains represent complex systems of organizations, people, activities, information, and resources involved in moving a product or service from supplier to customer. Supply Chain Management (SCM) solutions are typically manifest in software architecture and systems that facilitate the flow of information among different functions within and between enterprise organizations.

Leading SCM solutions catalyze information sharing across organizational units and geographical locations, enabling decision-makers to have an enterprise-wide view of the information needed in a timely, reliable, and consistent fashion. Various forms of Artificial Intelligence (AI) are being integrated into SCM solutions to improve everything from process automation to overall decision-making. This includes greater data visibility (static and real-time data) as well as related management information system effectiveness.

In addition to fully automated decision-making, AI systems are also leveraging various forms of cognitive computing to optimize the combined efforts of artificial and human intelligence. For example, AI in SCM is enabling improved supply chain automation through the use of virtual assistants, which are used both internally (within a given enterprise) as well as between supply chain members (e.g. customer-supplier chains). It is anticipated that virtual assistants in SCM will leverage an industry-specific knowledge database as well as company, department, and production-specific learning.

AI-enabled improvements in supply chain member satisfaction causes a positive feedback loop, leading to better overall SCM performance. One of the primary goals is to leverage AI to make supply chain improvements from production to consumption within product-related industries as well as create opportunities for supporting "servitization" of products in a cloud-based "as a service" model. AI will identify opportunities for supply chain members to have greater ownership of "outcomes as a service" and control of overall product/service experience and profitability.

With Internet of Things (IoT) technologies and solutions taking an ever-increasing role in SCM, the inclusion of AI algorithms and software-driven processes with IoT represents a very important opportunity to leverage the Artificial Intelligence of Things (AIoT) in supply chains. More specifically, AIoT solutions leverage the connectivity and communications power of IoT, along with the machine learning and decision-making capabilities of AI, as a means of optimizing SCM by way of data-driven managed services.

Key Topics Covered

1. Executive Summary

2. Introduction

2.1 Supply Chain Management

2.1.1 Challenges

2.1.2 Opportunities

2.2 AI in SCM

2.2.1 Key AI Technologies for SCM

2.2.2 AI and Technology Integration

3. AI in SCM Challenges and Opportunities

3.1 Market Dynamics

3.1.1 Companies with Complex Supply Chains

3.1.2 Logistics Management Companies

3.1.3 SCM Software Solution Companies

3.2 Technology and Solution Opportunities

3.2.1 Leverage Artificial Intelligence (AI)

3.2.1.1 Integrate AI with Existing Processes

3.2.1.2 Integrate AI with Existing Systems

3.2.2 Integrate AI with Internet of Things (IoT)

3.2.2.1 Leverage AIoT Platforms, Software, and Services

3.2.2.2 Leverage Data as a Service Providers

3.3 Implementation Challenges

3.3.1 Management Friction

3.3.2 Legacy Processes and Procedures

3.3.3 Outsource AI SCM Solution vs. Legacy Integration

4. Supply Chain Ecosystem Company Analysis

4.1 Vendor Market Share

4.2 Top Vendor Recent Developments

4.3 3M

4.4 Adidas

4.5 Amazon

4.6 Arvato SCM Solutions

4.7 BASF

4.8 Basware

4.9 BMW

4.10 C.H. Robinson

4.11 Cainiao Network (Alibaba)

4.12 Cisco Systems

4.13 ClearMetal

4.14 Coca-Cola Co.

4.15 Colgate-Palmolive

4.16 Coupa Software

4.17 Descartes Systems Group

4.18 Diageo

4.19 E2open

4.20 Epicor Software Corporation

4.21 FedEx

4.22 Fraight AI

4.23 H&M

4.24 HighJump

4.25 Home Depot

4.26 HP Inc.

4.27 IBM

4.28 Inditex

4.29 Infor Global Solutions

4.30 Intel

4.31 JDA

4.32 Johnson & Johnson

4.33 Kimberly-Clark

4.34 L'Oreal

4.35 LLamasoft Inc.

4.36 Logility

4.37 Manhattan Associates

4.38 Micron Technology

4.39 Microsoft

4.40 Nestle

4.41 Nike

4.42 Novo Nordisk

4.43 NVidia

4.44 Oracle

4.45 PepsiCo

4.46 Presenso

4.47 Relex Solution

4.48 Sage

4.49 Samsung Electronics

4.50 SAP

4.51 Schneider Electric

4.52 SCM Solutions Corp.

4.53 Splice Machine

4.54 Starbucks

4.55 Teknowlogi

4.56 Unilever

4.57 Walmart

4.58 Xilinx

5. AI in SCM Market Case Studies

5.1 IBM Case Study with the Master Lock Company

5.2 BASF: Supporting smarter supply chain operations with cognitive cloud technology

5.3 Amazon Customer Retention Case Study

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Global Artificial Intelligence (AI) in Supply Chain Management (SCM) Market 2022-2027 - Solutions as a Whole Will Reach $16.7B Globally by 2027 -...

With the intrusion of artificial intelligence and blockchain technology, the world has reached new heights recently

Many things have changed since the beginning of the 21st century. At the core of all these transformations, a simple concept called technology prevails. Yes, for the past two decades, technology has affected the way we live, work, learn, study, communicate, transport, and even think. As a result of modern trends intrusion, computers are becoming faster, more portable, and higher-powered than ever before. Although the tech evolution has both positive and negative impacts, the good side of the transformation is heavily admired by people.

It all started in 2000 when the dotcom bubble burst and gave birth to disruptive trends like the internet and the smartphone. Even though the stocks of many companies tumbled for a while, it paved the way for tech giants like Amazon to get a stronghold on the market. Many more people are online today than they were at the start of the millennium. The broadband expansion has also introduced artificial intelligence into mainstream adoption. One could argue that technology has continued to improve our lives, keeping us more connected to big data and with each other. But the amazing transformation has also paved the way for increasing complexities.

Today, everything starting from transport vehicles to medical devices, financial transactions, and electricity systems are relying on computer software. While digitization has made things easier for humankind, it has also made technology harder to control. When human-to-human contact is minimized with disruptive trends, it provides a space for machines to entertain bias and dominance.

Although the term artificial intelligence came into existence in the 1950s, it entered mainstream acceptance only in the 2000s. The core motto of developing artificial intelligence technology is to make machines imitate human behavior like thinking and taking decisions on their own. However, that kind of intelligence is yet to be achieved. Meanwhile, humans have come a long way from where they started the 21st on accords with technology. With amazing branches like data science, machine learning, robotics, and business intelligence rocking the digital sphere, modern artificial intelligence can understand data and make real-time decisions.

Initially, AI was intended to defeat more manageable issues like language recognition, playing a game, and picture recovery. With the innovative headways, artificial intelligence is getting progressively sophisticated at doing what people do, yet more effectively, quickly, and at a lower cost in tackling complex issues. Further, the outbreak of the Covid-19 pandemic took artificial intelligence to the next level. Even industries that were extremely slow in adopting technology embraced artificial intelligence at a quicker pace. Today, AI is playing an essential role in supporting and aiding decision-making in every walk of life.

If we step out of the AI ecosystem and enter the blockchain bubble, far more emerging trends are flying around like never before. From being a Bitcoin platform as conceived by Satoshi Nakamoto in 2009, blockchain has come a long way to emerge as a futuristic aspect in the digital sphere. It has reached far beyond the originally planned cryptocurrency realm. Furthermore, blockchain is expanding its wings through new features like decentralized applications, smart contracts, metaverse, and NFTs.

In a nutshell, it looks like artificial intelligence and blockchain technology are here to stay. If you want to be a part of this trailblazing revolution, know more about the important trends in this tech space.

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Artificial Intelligence and Blockchain at the Heart of Modern Tech Evolution - Analytics Insight

By Richard Shearer, CEO of Tintra PLC

In the modern banking world new technologies play a widely reported role in anti-money laundering (AML) protocols preventing financial crime however it is important that we do not overlook technologys potential for establishing financial innocence.

To businesses and institutions operating in and between developed markets, whose international transactions are fast and painless, this sentiment may seem counter intuitive. AML compliance is necessary for regulatory reasons, and catching out bad actors is, of course, a primary goal of any business but why should we view AML technology through the lens of establishing innocence?

This is a question which emerging market corporates will have no difficulty answering if they have ever attempted to interface with counterparts in developed markets.

Entities based in emerging markets are often tarred with the brush of AML risk due to their geography and unrelated to their specific business, and consequently such organisations find international transactions lengthy, arduous and expensive as they navigate an AML compliance process that operates from a base level that is an unfair assumption of their risk.

As such, in my view, embracing advances in technologies such as natural language processing (NLP) and machine learning (ML) is essential not only for financial services firms looking to enhance their ability to properly mitigate, but to progress the much bigger, and indeed more noble, goal of removing the biases against emerging markets, nationalities or cultures that currently colour the AML landscape.

How then, can NLP and ML technologies help, not only in addressing financial crime, but in creating an environment where those in emerging markets with upstanding credentials are treated and serviced free from these baked in prejudices?

Intelligent machines

Its worth taking a moment to define these terms.

Natural Language Processing pertains, in broad terms, to anything related to processing language. As such, NLP varies in terms of complexity it may be employed for tasks like term frequency, calculating how often a given word appears in a text, but NLP can equally be used for the purposes of translation; classifying the sentiment of a piece of text; or even detecting sarcasm, irony, and fake news in a social media context.

In order to perform the more complex tasks in this spectrum however, machine learning may also be required.

Machine Learning describes a variety of artificial intelligence (AI) with an emphasis on allowing machines to learn in a similar manner to humans, through a mix of data and algorithmic methods.

ML differs from traditional programming. Traditional programs see a solution to a problem defined through hand-crafted rules that are implemented in computer code. In ML, by contrast, the algorithm itself learns those rules and, by extension, how to solve the problem by analysing data.

This principle makes ML considerably more powerful than traditional programming, since it is capable of learning a complicated sets of rules that are impossible to define manually.

AML applications of these Technologies

In the context of AML practices, its not difficult to see the appeal of technology like this.

After all, manual investigations into potentially rogue activities are lengthy processes which involve employees investigating vast swathes of transaction histories and other information and often only happen after the event.

This process is made all the more difficult to manage for financial institutions when a large number of suspicious incident alerts are often false alarms. But each potential issue must be investigated with the same vigour to ensure a robust AML framework.

By contrast NLP/ML allows financial institutions to automate these processes the more sophisticated solutions, that my team and I are very focused on, are capable of interpreting the vast amounts of text-based data that a human would otherwise need to analyse.

These systems are able to recognise patterns and relevant information, consider appropriate context and cross reference faster and more accurately than a human, or indeed teams of humans, may overlook.

Crucially for me, NLP/ML performed by intelligent machines capable of learning can potentially undertake these tasks at the same time as neutralising human bias, which has promising implications for organisations and individuals in emerging markets who face these preconceptive biases frequently.

Less human, more humane

This application of NLP/ML has a range of benefits for all stakeholders, not least with reductions in the level of false positives representing savings in time and money for financial services companies.

There is, however, equal value to be found in NLP/ML tools which bring this power to bear on addressing the inequities that currently prevent frictionless transactions between these markets.

This piece began with reference to establishing cases of financial innocence as well as financial crime and, while NLP/ML makes this possible, it would be wrong to assume that such tools will magically resolve the issue of AML bias.

As such, establishing innocence isnt just a different perspective on the benefits of NLP/ML solutions its an ethos that I believe should be actively pursued by financial services businesses as our global economy becomes more and more integrated.

Removing human prejudice from the decision-making process is vital, but a truly fair approach can only be achieved when the creators of these solutions acknowledge that the prejudice exists in the first place.

After all, NLP/ML is entirely subject to bias or algorithmic unfairness,. A good example taken from research published in ACM Computing Surveys is a piece of software called COMPAS, used by US judges to assess offenders risks of reoffending, which was found to exhibit bias against African-American individuals illustrating clearly that human prejudice can inflect algorithmic decision-making. To make the technology better we need to be better, is may be one way of thinking about it.

This kind of example gives food for thought. If NLP/ML tools are trained without thought being given to how to eradicate bias in an AML context, then well be left with intelligent machines that simply replicate that bias meaning that prejudice will be automated rather than eliminated! A terrifying concept and one fraught with complex ethics.

The next step

The financial services sector is in the midst of digital transformation and as such the time is ripe to seize the wheel and ensure, as we embrace more sophisticated tech solutions, that the journey ends at a fair and equitable destination no matter where a given transaction takes place.

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Artificial Intelligences Role in Banking 3.0 - Global Banking And Finance Review