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A recentViewpoint inJAMAhighlighted the importance of properly introducing clinicians to, and training them in, artificial intelligence (AI) and machine learning (ML) technologies. The piecewritten byCornelius James, M.D., a clinical assistant professor of internal medicine and pediatrics; James Woolliscroft, M.D., professor of medicine and former dean of U-Ms Medical School; and Bob Wachter, M.D., professor and chair of UCSFs Department of Medicine echoes the theme of U-Ms Precision Healthswebinar series for the 2021-22 academic year(Demystifying the Data, Processes, and Tools that Are Changing Clinical Care).

In theJAMAViewpoint, the co-authors state, it is likely that every medical specialty will be influenced by AI, and some will be transformed. Because of this, they stress that multiple levels of oversight are needed, as well as clinician review and trust of these technologies.

The authors suggest that physicians welcome skepticism, avoid cynicism when it comes to AI. While it is true that AI applications have yet to display rigorous evidence of consistent success and benefit in clinical settings, clinicians nevertheless need to have a realistic understanding of the potential uses and limitations of medical AI applications to sidestep cynicism and optimize patient outcomes. Also, as patients become more active in their medical decision-making processes, it will fall to clinicians to successfully broker the triadic relationship between patients, the computer, and themselves.

AI/ML will play an increasingly important role in many facets of health care. This means frontline clinicians will need to know how to engage with these technologies in an effective and efficient manner as they make medical decisions, says James. We must not wait until this technology is ubiquitous to begin discussing the important role that clinicians should play in the development, deployment, and implementation of AI/ML models in health care. In this Viewpoint, we provide recommendations to ensure that not only will clinicians be ready for AI in health care, but that they are able to play an integral role in preparing patients as well.

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Preparing clinicians for artificial intelligence, machine learning - Michigan Medicine

AdTheorent's performance-first platform drove a 68% engagement rate and delivered a Return on Ad Spend that exceeded benchmark by 117%

NEW YORK, April 14, 2022 /PRNewswire/ -- AdTheorent Holding Company, Inc. ("AdTheorent" or the "Company") (Nasdaq: ADTH), a leading programmatic digital advertising company using advanced machine learning technology and privacy-forward solutions to deliver measurable value for advertisers and marketers, today announced campaign results from a recent digital fundraising campaign for American Cancer Society (ACS). The campaign goal was to drive cost-effective donations and positive Return on Ad Spend (RoAS), as well as raise awareness of ACS. The campaign drove strong donations revenue, yielding an overall campaign RoAS which was 2-times more efficient than the ACS target benchmark.

The Approach:

AdTheorent worked with Tombras, media agency of record for ACS, to drive efficient donations and achieve a strong RoAS, in addition to increasing awareness of the brand's core areas of focus, including: advocacy, discovery, and patient support. In order to achieve the dual-pronged objectives, AdTheorent leveraged a mix of cross-device rich media, interactive banners and display tactics, targeted using AdTheorent's advanced predictive advertising platform. AdTheorent developed custom machine learning models fueled by non-individualized statistics to identify and reach consumers with the highest likelihood of completing the required campaign actions. AdTheorent's programmatic performance optimizers utilized myriad signals in the custom predictive models such as ad position, publisher, geo-intelligence, non-individualized user device attributes, location DMA, time of day, connection signal and many others to find the most qualified users and reach ACS' target audience of prospective donors, current donors, and lapsed donors, with a national footprint. Additionally, AdTheorent utilized real-time contextual signals to identify and reach consumers engaging with content related to ACS or charitable donations. Through in-unit pixel placement, user engagement fueled targeting allowing AdTheorent to optimize in real-time and scale targeting to drive results for each targeting tactic.

"Every dollar raised helps the American Cancer Society improve the lives of people with cancer and their families as the only organization that integrates advocacy, discovery and direct patient support," said Ben Devore, Director, Media Strategy at ACS. "Every bit of our campaign spend needs to be optimized for the best possible performance, so our key advertising goal was to reach the most probable donors, and then engage them in a way that would drive donations. AdTheorent helped us outperform our KPIs, with a very efficient return on ad spend and an exceptionally high engagement rate of nearly 70% throughout the duration of the campaign which helps our organization achieve greater impact, overall."

The Results:

The campaign exceeded all benchmarks across all tactics:

AdTheorent's data driven-platform identified targeting variables which yielded conversion lift, providing valuable insights for future flights of the campaign.

"AdTheorent Predictive Advertising uses advanced machine learning and data science to drive real-world performance and advertiser ROI in the most privacy-forward and efficient manner," said James Lawson, CEO at AdTheorent. "We are honored to work with Tombras and ACS to further ACS's vital mission. And we are proud of the results we have helped produce, driving donation revenue at an efficiency rate 2X greater than ACS expectations."

About AdTheorent

AdTheorent uses advanced machine learning technology and privacy-forward solutions to deliver impactful advertising campaigns for marketers.AdTheorent's industry-leading machine learning platform powers its predictive targeting, geo-intelligence,audience extension solutions and in-house creative capability, Studio AT.Leveraging only non-sensitive data and focused on the predictive value of machine learning models, AdTheorent'sproduct suite and flexible transaction models allow advertisers to identify the most qualified potential consumers coupled with the optimal creative experience todeliver superior results, measured by each advertiser's real-world business goals.

AdTheorent is consistently recognized with numerous technology, product, growth and workplace awards. AdTheorent was awarded "Best AI-Based Advertising Solution" (AI Breakthrough Awards) and "Most Innovative Product" (B.I.G. Innovation Awards) for four consecutive years. Additionally, AdTheorent is the only six-time recipient of Frost & Sullivan's "Digital Advertising Leadership Award."AdTheorent is headquartered in New York, with fourteen offices across the United States and Canada. For more information, visit adtheorent.com.

About Tombras

Tombras is a 430+ person full service, independent advertising agency headquartered in Knoxville, Tennessee connecting data and creativity for business results. Named a FastCo Most Innovative Company, to the AdAge A-List and a Most Effective Independent Agency per Effie Worldwide. Tombras is one of the fastest growing full-service independent agencies with offices in New York, Atlanta, Washington, D.C., Charlotte, NC, and headquarters in Knoxville. Tombras works with notable brands including American Cancer Society, Big Lots, MoonPie, Mozilla Firefox, Orangetheory Fitness, Pernod Ricard and others. More information:tombras.com.

About American Cancer Society

The American Cancer Society is on a mission to free the world from cancer. We invest in lifesaving research, provide 24/7 information and support, and work to ensure that individuals in every community have access to cancer prevention, detection, and treatment. For more information, visit cancer.org.

SOURCE AdTheorent

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AdTheorent Uses Machine Learning-Powered Predictive Advertising to Boost Donations and Drive Awareness for American Cancer Society - PR Newswire

When we think about artificial intelligence, what comes to mind might be HAL 9000 from 2001: A Space Odyssey, Skynet from Terminator, and even Ultron from Marvel (a.k.a. Tony Starks mad AI experiment). You know, robots are going to take over the world. Humanity is doomed. Computers for life. The matrix is real

Thankfully, not all AIs are so destructive and dangerous. For every Terminator, theres also R2-D2, WALL-E, and Data from Star Trek. Suffice to say, Earth is safe for now, and chances are low that well find ourselves in a world where machines take over and force humans to fight as an underground resistance.

Most of the AI we see in films and series are still works of fiction. Whether we get to that level, only time can tell. But be that as it may, there are many advanced applications of AI in everyday life, including AI in contract management.

Before we dig deeper into how AI plays a role in CLM, here are a couple of key definitions.

What is Artificial Intelligence?

AI attempts to replicate humans intelligence or behavioral patterns. Theoretically, an AI can be created based on some other living entity, so long as its intelligence or behavior can be mapped, but for simplicity, lets assume all AI in legal technology tries to replicate human behavior.

Creating an AI requires the application of techniques that enable computers to solve problems as if it were a human. This usually takes the form of a set of pre-defined rules.

What is Machine Learning?

Within the larger sphere of AI, ML is a field comprised of four subcategories: supervised learning, unsupervised learning, reinforced learning, and deep learning. Its a technique that allows computers to learn from data. In other words, its a type of AI that learns by itself.

Often, ML is conducted by training a computational model (also known as an algorithm) using a dataset. The more time and data provided, the better the computers performance.

A subset of machine learning is deep learning. This attempts to use artificial neural networks, which are computer systems that imitate the human brains neural network. These ANNs are designed so that an increasing number of characteristics are extracted from the input data.

In simpler terms, deep learning is simply machine learning, with the key difference being that the model is based on human neural networks.

Machine Learning vs Artificial Intelligence

Some people use AI and ML interchangeably, as if they were perfect synonyms. Others might refer to them as completely separate, parallel examples of advanced technology. Sometimes this is because both words are overhyped, and sometimes its because people dont quite get the difference.

The short answer is that AI and ML are not the same.

As we mentioned earlier, machine learning is a field of AI. Theyre closely related, yet still different. All machine learning counts as artificial intelligence, but not all AI counts as machine learning. AI, in fact, can be divided into four primary parts: Reasoning, Natural Language Processing (NLP), Planning, and Machine Learning.

AI can be divided into four primary parts: Reasoning, Natural Language Processing (NLP), Planning, and Machine Learning.

An example of an AI that doesnt use ML is a chatbot. Certainly, there are chatbots that use ML, but a basic chatbot doesnt require it. Thats because its a rule-based system whose rules (the questions it asks and answers it receives) were defined by people. These sorts of expert systems are essentially a sequence of if this, then that statements, otherwise known as a decision tree. Although no learning occurs, such rule-based chatbots can still prove highly useful.

But lets imagine we decide to create an ML-based chatbot. Whereas all questions and answers were previously scripted for a rule-based bot, an ML-based bot is given a massive corpus containing hundreds of thousands of conversations to clean up and analyze. Then based on the information provided, the ML-based bot is trained to handle various situations.

AI and ML in everyday life

Most likely, you interact with some tool or program that uses AI or ML, such as any Google product. Major services like Gmail, Google Search, and Google Maps all have ML. For example, how emails are filtered into the primary, social, and promotional tabs, or how Search and Maps anticipate what youre looking for based on past searches, trends, and even your location.

Then there are assistants like Siri and Alexa, social media newsfeeds, Netflix, Amazon, and even smart home devices. As the world becomes increasingly digital and smart functionalities become more ubiquitous, its almost certain that youll be interacting with ML-based products and services on an hourly basis.

AI and ML techniques in contract management solutions

To quickly recap AI and ML, think of artificial intelligence as an attempt to give machines the intelligence of people. In contract management, this would enable an AI to read and interpret agreements while extracting key information.

Machine Learning is the process of teaching or training the AI by exposing it to massive quantities of documents. The larger the dataset, the better the AIs accuracy and the more stable its performance.

But one critical technique that hasnt been highlighted is NLP, which is the ability for software to recognize and mimic a persons speech. This approach helps identify and resolve potentially trick spots in the language. Its also used when contracts are being drafted, when spotting missing contract aspects, and categorizing contract attributes with regards to the context.

Role of AI in CLM

Currently, there are three core roles that artificial intelligence plays in contract lifecycle management:

Data extraction from active contracts

With the help of ML and Natural Language Processing, legacy contracts and other documents you had before implementing a CLM software system can be quickly imported to the new CLM platform. This is of critical importance if any of the contracts and agreements are still active. To avoid complacency and missed deadlines, its a good idea to upload legacy contracts so that the new CLM software can do its job of managing obligations and generating notifications.

Since this can be done using ML and NLP, the importing process takes considerably less time. Teams no longer need to open PDFs, read terms, save them in Excel, and manually track them. Thats because both ML and NLP help identify a contracts metadata, such as the due dates, deliverables, and signing parties. Better yet, technology is good enough that even if a contract is a scanned image (this might be the case if your signed contract is only in paper form), information can still be extracted.

This kills two birds with one stone because at the same time that youre importing legacy contracts to the new CLM platform, youre training the ML algorithm on your contracts. That means the ML will be more capable of handling your future contracts.

Assistance during contract authoring

During the stone age of CLM software, processes were simply made available in a digital format. For example, the system would simply provide fields that could be filled in with data.

CLM software has come a long way since that, in part due to significant advancements in AI. Trained on thousands of contracts from several years, a quality AI-based CLM software can act like a senior advisor by reviewing contracts and suggesting opportunities for negotiation and risk management. Assuming your organization has a clause library, it can even provide recommendations on which clauses to use according to geography, vendor type, and contract value.

Some CLM platforms even offer a chatbot for the contract authoring process. Not all are powered by ML, but they add a great deal of value for users by helping fill out contracts with the answers to select questions.

Management of contract obligations

Arguably AIs biggest role in CLM, an AI can extract obligations automatically from contracts (including uploaded legacy documents) to manage them. At present, AI still has some problems when extracting non-quantifiable obligations like IP protection and employee welfare. But when the language features quantifiable terms like discounts and due dates, AI works like a charm.

Major applications of AI in contract management

As mentioned previously, AI has come a long way since its inception. The meaning and techniques used have consistently evolved over the years until weve become dependent on AI to simplify common problems. Google and Netflix are major examples of the strides that AI has made in solving peoples problems, but other industries like medicine and finance have also been affected. Legal is no different.

How an AI-based CLM can solve contract management issues

Here are just a few of the ways that AI can address common problems of contract management:

Batch review

Instead of checking one document at a time like a person would, an AI can review multiple agreements, update terms, and import legacy contracts in batches. For example, if your government regulator changes certain legal requirements, you can input the fix across multiple documents at the click of a button.

Risk

The more contracts your organization handles, the more likely there will be missed renewals, fee increases, and compliance problems. The failure to abide by contract terms is one of the leading causes of commercial legal disputes. An AI-based CLM is capable of generating the reminders you need and monitoring obligations so that your organization stays compliant.

Time

Its been said before, and itll be said later, but the consequences of saving time are massive. Not only do you save money as a direct result, but your employees will have greater freedom to dedicate their efforts to more productive and professionally rewarding tasks.

AI can be used to help overcome challenges that frequently appear as contracts make their way through the workflow. But thats not all. With the right approach, AI can unlock innovative, new possibilities.

For instance, GPT-3, which is a neural network model that uses deep learning to generate any type of text, opens up countless opportunities. Its ability to perform reading comprehension and writing at near-human levels stems in part from having consumed more text than any human can ever read in their lifetime.

Reasons to use AI in CLM

There are a lot of excellent reasons to use artificial intelligence in contract lifecycle management. But first and foremost, the top three reasons are that it saves a lot of time, a lot of money, and a lot of effort. Processes can be completed 80% quicker (which in turn saves money), and a lot of repetitive tasks can be carried out automatically.

Those are just some big picture reasons though. Here are some specific benefits that AI can provide legal companies and legal teams:

Automated contract compliance

An AI CLM system can be configured to take into account regulatory and contractual compliance terms that are industry or jurisdiction specific. Also, in addition to setting notifications for specific team members regarding dates and obligations, an entire audit trail can be created.

Maximized employee expertise

AI is unable to operate with complete independence. There must always be somebody overseeing its performance in order to guide and fine-tune it, or to step in if theres an anomaly or problem. As a result, companies can leverage and maximize team members expertise while freeing them from repetitive, less interesting tasks.

Standardized contractual processes

AI can ensure that theres consistency across the entire CLM process by putting every contract through the same sequence of stages. Not only is this the most efficient approach to managing contract lifecycles, but it enhances visibility from start to finish while simplifying the identification of any contracts with issues to be resolved.

Improved connectivity

Robust, organized systems can unlock countless opportunities for companies thanks to the increase in efficiency. AI contract software can clean up and standardize data so that it can be easily integrated with other powerful tech. Otherwise, if data remains inconsistent, and if storage and retrieval systems are outdated, then teams will miss out on the advantages that automated CLM solutions can provide.

Efficient scaling

If an AI-based CLM solution has made data standardized, compliance automated, and visibility ensured, teams can scale and add layers of management without becoming overly complex and inefficient. Thats because an AI CLM software system doesnt need an increase in personnel to keep a growing organization running smoothly.

Conclusion

One of the most important things to remember about AI in contract management is that its not about to steal anyones job. Like we mentioned before, AI is best deployed in a supporting role where it can provide critical assistance in speeding up contract processes. Although modern AI has made great strides, it cant function 100% independently. But if deployed correctly, it can speed up the contract workflow by up to 80%.

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Reap the Benefits of AI in CLM | AXDRAFT (an Onit company) - JDSupra - JD Supra

Creating new mathematical conjectures and theorems needs a complex approach which requires three factors that are:

At DeepMind, a UK-based artificial intelligence laboratory, researchers in collaboration with mathematicians at the University of Oxford, UK, and University of Sydney, Australia, respectively. The researchers over there have made an important breakthrough by using machine learning to highlight the mathematical connections that human counterparts miss.

Into the technology behind DeepMind

In fascination with the way humans usually used to think and human-based intelligence has long caught the image of computer scientists. Human intelligence has en-sharpened the digital modern world, thus allow us to learn, create, communicate and develop by our own self-awareness.

Since 2010, researchers and developers at the DeepMind team have been trying to solve intelligence-based problems, developing problem-solving systems that are an Artificial General Intelligence (AGI).

In order to perform, DeepMind takes an interdisciplinary approach that commits machine learning and neuroscience, philosophy, mathematics, engineering, simulation, and computing infrastructure together.

The company has already made significant breakthroughs with its machine learning and AI systems, for example, the AlphaGo program, which was the first AI to beat a human professional Go player.

Thinking DeepMaths

The work developed by the DeepMind team says that mathematicians can benefit from machine learning tools to sharpen up and enhance up their intuition where complex mathematical objects and their relationships are highly concerned.

Initially, the project was focused on identifying mathematical conjectures and theorems that DeepMinds technology could deal with, but ultimately it is all dependent upon probability as opposed to absolute certainty.

However, when dealing with large sets of information, the researchers tried to apply their own intuition that the AI could detect the signal relationships between mathematical objects. Afterward, the mathematicians could then apply their own conjecture to the relationships to make them an absolute certainty.

Tied up in Knots

Machine learning requires several amounts of data in order to complete the task efficiently and effectively. So the researchers tied knots as their starting point, calculating invariants.

DeepMinds AI software was assumed to work on two separate components of knot theory; algebraic and geometric. The team then used the program to seek relationships between straightforward and complex correlations as well as subtle and unintuitive ones.

The leads presenting the most promising data were then directly handed over to human mathematicians for analysis and refinement.

The DeepMind team believes that mathematics can release the benefits from methodology and technology as an effective mechanism that could see the widespread application of machine learning in mathematics. Thus, this strengthens the relationship between methodology and technology.

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Machine learning to create some of the new mathematical conjectures - Techiexpert.com - TechiExpert.com

Predicting Growth Scope: Predictive Analytics And Machine Learning MarketThe Predictive Analytics And Machine Learning Market report provides an in-depth look at service providers and how their business policies are implemented in the market. The Predictive Analytics And Machine Learning market research report examines market share, size, growth drivers, and major players in detail. In addition to evaluating the sectors financial position, the report provides an inclusive market and dealer climate. Input, market size, sales income, growth rate, revenue, demand, gross margin, technological innovation, supply, import, export, expense, and potential growth strategies are all covered in this report.

The goal of this worldwide Predictive Analytics And Machine Learning market study is to outline the industrys current state as well as its future prospects. It investigates new Predictive Analytics And Machine Learning competitors and changing customer behavior to help market participants make better judgments. The survey helps market participants choose which issues and regions are most important to them. It investigates the growth of present and emerging categories, as well as the revenue performance of the Predictive Analytics And Machine Learning industry.

Competition Spectrum:Schneider ElectricSAS Institue Inc.MakinaRocks Co., Ltd.Globe Telecom,Inc.QlikRapidMinerIBMAlteryxAlibaba GroupHuaweiBaidu4Paradigm

The research on the worldwide keyword market covers a wide range of qualitative and quantitative analytical results that reflect a variety of crucial characteristics that define the keyword markets current state. The qualitative data supports the categorised assessment of the critical growth inducing elements indicating important drivers driving the worldwide keyword markets growth, either influencing demand or generating income. The report uses current quantitative market share and size scales, as well as industry valuation using the above-mentioned qualitative elements, to arrive at an accurate projection of the worldwide keyword market.

We Have Recent Updates of Predictive Analytics And Machine Learning Market in Sample [emailprotected] https://www.orbisresearch.com/contacts/request-sample/5517146?utm_source=PoojaGIR1

The report highlights the nations that are growing in demand and also the nations where the demand for the Predictive Analytics And Machine Learning market products and services is contracted. It highlights the worlds largest producers of the Predictive Analytics And Machine Learning market products and the consumption of the products in million tons. The foreign and domestic demand for the products in mn tons is also given in the report. Moreover, the factors driving the increased demand in the selected nations are also studied. The challenges for the market participants including the cost competitiveness of the raw materials, competition from imports, and technology obsolescence are included in the report.

The market is roughly segregated into:

Analysis by Product Type:General AIDecision AI

Application Analysis:FinancialRetailManufactureMedical TreatmentEnergyInternet

The report investigates the Predictive Analytics And Machine Learning market in all industrial segments and identifies opportunities to modify the competitive climate. The research looks at end-user groups, analyses emerging applications, and analyses market participants methods for keeping ahead of the competition.

Segmentation by Region with details about Country-specific developments North America (U.S., Canada, Mexico) Europe (U.K., France, Germany, Spain, Italy, Central & Eastern Europe, CIS) Asia Pacific (China, Japan, South Korea, ASEAN, India, Rest of Asia Pacific) Latin America (Brazil, Rest of L.A.) Middle East and Africa (Turkey, GCC, Rest of Middle East)

Table of Contents Chapter One: Report Overview 1.1 Study Scope1.2 Key Market Segments1.3 Players Covered: Ranking by Predictive Analytics And Machine Learning Revenue1.4 Market Analysis by Type1.4.1 Predictive Analytics And Machine Learning Market Size Growth Rate by Type: 2020 VS 20281.5 Market by Application1.5.1 Predictive Analytics And Machine Learning Market Share by Application: 2020 VS 20281.6 Study Objectives1.7 Years Considered

Chapter Two: Growth Trends by Regions 2.1 Predictive Analytics And Machine Learning Market Perspective (2015-2028)2.2 Predictive Analytics And Machine Learning Growth Trends by Regions2.2.1 Predictive Analytics And Machine Learning Market Size by Regions: 2015 VS 2020 VS 20282.2.2 Predictive Analytics And Machine Learning Historic Market Share by Regions (2015-2020)2.2.3 Predictive Analytics And Machine Learning Forecasted Market Size by Regions (2021-2028)2.3 Industry Trends and Growth Strategy2.3.1 Market Top Trends2.3.2 Market Drivers2.3.3 Market Challenges2.3.4 Porters Five Forces Analysis2.3.5 Predictive Analytics And Machine Learning Market Growth Strategy2.3.6 Primary Interviews with Key Predictive Analytics And Machine Learning Players (Opinion Leaders)

Chapter Three: Competition Landscape by Key Players 3.1 Top Predictive Analytics And Machine Learning Players by Market Size3.1.1 Top Predictive Analytics And Machine Learning Players by Revenue (2015-2020)3.1.2 Predictive Analytics And Machine Learning Revenue Market Share by Players (2015-2020)3.1.3 Predictive Analytics And Machine Learning Market Share by Company Type (Tier 1, Tier Chapter Two: and Tier 3)3.2 Predictive Analytics And Machine Learning Market Concentration Ratio3.2.1 Predictive Analytics And Machine Learning Market Concentration Ratio (Chapter Five: and HHI)3.2.2 Top Chapter Ten: and Top 5 Companies by Predictive Analytics And Machine Learning Revenue in 20203.3 Predictive Analytics And Machine Learning Key Players Head office and Area Served3.4 Key Players Predictive Analytics And Machine Learning Product Solution and Service3.5 Date of Enter into Predictive Analytics And Machine Learning Market3.6 Mergers & Acquisitions, Expansion Plans

Do You Have Any Query or Specific Requirement? Ask Our Industry [emailprotected] https://www.orbisresearch.com/contacts/enquiry-before-buying/5517146?utm_source=PoojaGIR1

A critically emphasised section of the report that primarily focuses on the influence of COVID-19 on the worldwide keyword market follows a detailed examination of the multi-variable industry dynamics. The paper includes a concise and in-depth analysis of the major differences between the pre-pandemic and post-pandemic eras. The research considers the exact magnitudes of adversities on market share, general infrastructure, financial state, rate of demand, revenue incurred, supply chain, and production capacities of the keyword market on a worldwide scale. The paper focuses on the unique changes in market dynamics that characterise the pandemics short- and long-term consequences on the worldwide keyword market.

Key Pointers of the Predictive Analytics And Machine Learning Market Report: The study analyses the industrys leading firms and their market share. The research provides strategies that may improve market performance across the board. The study offers a variety of alternatives for benchmarking against the rest of the market as well as best practices for competing in the market. The paper examines the influence of changing megatrends on the operating environment, supply chain, and entire business. The study discusses the influence of new technologies on the worldwide Predictive Analytics And Machine Learning market as well as the impact of introducing new business models. The research highlights future potential for both new and incumbent companies.

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Predictive Analytics And Machine Learning Market Focus on End User, Application, Solution, Component, and Range: Schneider Electric, SAS Institue...