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Theres been a lot of discussion on the use of artificial intelligence and the future of work. Will it replace workers? Will human creativity be usurped by bots? How will A.I. be incorporated into the finance function? These are just some of the questions organizations will face.

I asked Sayan Chakraborty, copresident at Workday (sponsor of CFO Daily), who also leads the product and technology organization, for his perspective on a balance between tech and human capabilities.

Workdays approach to A.I. and machine learning (ML) is to enhance people, not replace them, Chakraborty tells me. Our approach ensures humans can effectively harness A.I. by intelligently applying automation and providing supporting information and recommendationswhile keeping humans in control of all decisions. He continues, We believe that technology and people, working together, can allow businesses to strengthen competitive advantage, be more responsive to customers, deliver greater economic and social value, and generate more meaning and purpose for individuals in their work.

Workday, a provider of enterprise cloud applications for finance and HR, has been building and delivering A.I. and ML to customers for nearly a decade, according to Chakraborty. He holds a seat on the National Artificial Intelligence Advisory Committee (NAIAC), which advises the White House on policy issues related to A.I. (And as much as I pressed, Chakraborty is not at liberty to discuss NAIAC efforts or speak for the committee, he says.) But he did share that generative A.I. continues to be a growing part of policy discussions both in the U.S. and in Europe, which has embraced a risk-based approach to A.I. governance.

Techs future in finance

Chakrabortys Workday colleague Terrance Wampler, group general manager for the Office of the CFO at Workday, has further thoughts on how A.I. will impact finance. If you can automate transaction processes, that means you reduce risk because you reduce manual intervention, Wampler says. Finance chiefs are also looking for the technology to help in accelerating data-based decision-making and recommendations for the company, as well as play a role in training people with new skills, he says.

Consulting firm Gartner recently made three predictions on financial planning and analysis (FP&A) and controller functions and the use of technology:

By 2025, 70% of organizations will use data-lineage-enabling technologies including graph analytics, ML, A.I., and blockchain as critical components of their semantic modeling.

By 2027, 90% of descriptive and diagnostic analytics in finance will be fully automated.

By 2028, 50% of organizations will have replaced time-consuming bottom-up forecasting approaches with A.I.

Workday thinks about and implements A.I. and ML differently than other enterprise software companies, Wampler says. I asked him to explain. Enterprise resource planning (ERP) is a type of software that companies use to manage day-to-day business activities like accounting and procurement. What makes Workdays ERP for finance and HR different is A.I. and ML are embedded into the platform, he says. So, its not like the ERP is just using an A.I. or ML program. It is actually an A.I. and ML construct. And having ML built into the foundation of the system means theres a quicker adaptation of new ML applications when theyre added. For example, Workday Financial Management allows for faster automation of high-volume transactions, he says.

ML gets better the more you use it, and Workday has over 60 million users representing about 442 billion transactions a year, according to the company. So ML improves at a faster rate. The platform also allows you to use A.I. predictively. Lets say an FP&A team has its budget for the year. Using ML, they predictively identify reasons why they would meet that budget, he says. And Workday works on a single cloud-based database for both HR and financials. You have all the information in one place. For quite some time, the company has been using large language models, the technology that has enabled generative A.I., Wampler says. Workday will continue to look into use cases where generative A.I. can add value, he says.

It will definitely be interesting to have a front-row seat as technology in the finance function continues to evolve over the next decade.

Sheryl Estradasheryl.estrada@fortune.com

Upcoming event: The nextFortuneEmerging CFO virtual event, Addressing the Talent Gap with Advanced Technologies, presented in partnership with Workday (a CFO Daily sponsor), will take place from 11 a.m.-12 p.m. EST on April 12. Matt Heimer, executive editor of features atFortune, and I will be joined byKatie Rooney, CFO at Alight Solutions; andAndrew McAfee, cofounder and codirector of MITs Initiative on the Digital Economy and principal research scientist at MIT Sloan School of Management.Click here to learn more and register.

The race to cloud: Reaching the inflection point to long-sought value, a report by Accenture, finds that over the past two years, theres been a surge in cloud commitment, with more than 86% of companies reporting an increase in cloud initiatives. To gauge how companies today are approaching the cloud, Accenture asked them to describe the current state of their cloud journeys. Sixty-eight percent said they still consider their cloud journeys incomplete. About a third of respondents (32%) see their cloud journeys as complete and are satisfied with their abilities to meet current business goals. However, 41% acknowledge their cloud journeys are ongoing and continue to evolve to meet changing business needs. The findings are based on a global survey of 800 business and IT leaders in a variety of industries.

The workforce well-being imperative, a new report by Deloitte, exploresthree factors that have a prominent impact on well-being in todays work environment: leadership behaviors at all levels, from a direct supervisor to the C-suite; how the organization and jobs are designed; and the ways of working across organizational levels. Deloitte refers to these as work determinants of well-being.

Lance Tucker was promoted to CFO at Papa Johns International, Inc. (Nasdaq: PZZA). Tucker succeeds David Flanery, who will retire from Papa Johns after 16 years with the company. Flanery will continue at the company through May, during a transition period. Tucker, 42, has served as Papa Johns SVP of strategic planning and chief of staff since 2010. He has 20 years of finance and management experience, including previously serving in manager and director of finance roles at Papa Johns from 1994 to 1999. Before Papa Johns, Tucker was CFO of Evergreen Real Estate, LLC.

Narayan Menon was named CFO at Matillion, a data productivity cloud company. Menon brings over 25 years of experience in finance and operations. Most recently, Menon served as CFO of Vimeo Inc., where he helped raise multiple rounds of funding and took the company public in 2021. Hes also held senior executive roles at Prezi, Intuit, and Microsoft. Menon also served as an advisory board member for the Rutgers University Big Data program.

This was a bank that was an outlier.

Federal Reserve Chair Jerome Powell said of Silicon Valley Bank in a press conference following a Fed decision to hike interest rates 0.25%, Yahoo Finance reported. Powell referred to the banks high percentage of uninsured deposits and its large investment in bonds with longer durations. These are not weaknesses that are there at all broadly through the banking system, he said.

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A.I. and machine learning are about to have a breakout moment in finance - Fortune

LONDON, UK, March 23, 2023 (GLOBE NEWSWIRE) -- Crypto AI ($CAI), an AI-powered NFT generator that uses machine learning algorithms to create unique digital assets, has announced its official launch in March 2023. The project aims to revolutionize the NFT space by combining the power of artificial intelligence and machine learning.

Crypto AI ($CAI) is a software application that generates NFTs through a proprietary algorithm that creates unique digital assets. These assets can then be sold on various NFT marketplaces or used as part of a larger project.

Discover What Crypto AI Do

Crypto AI Strives to Disrupt the NFT and Chat GPT space using Artificial Intelligence and Machine Learning.

Martin Weiner, the CEO of Crypto AI, stated, "We are excited to announce the official launch of Crypto AI, an AI-powered NFT generator that uses machine learning algorithms to create unique digital assets. Our goal is to disrupt the NFT space by offering a product that can generate truly unique NFTs that stand out in the marketplace."

Weiner went on to explain the key features of Crypto AI that sets it apart from other NFT generators. "What sets Crypto AI apart is the power of our proprietary algorithm. Our algorithm uses advanced machine learning techniques to create unique digital assets that are truly one-of-a-kind. Our AI-powered NFT generator is not only faster than traditional methods, but it is also more accurate and efficient."

Crypto AI aims to offer a new way for artists and creators to monetize their work through NFTs. The project believes that AI-powered NFTs will help increase the value of digital assets and make them more accessible to a broader audience.

Weiner added, "We believe that AI-powered NFTs have the potential to revolutionize the art world by making it more inclusive and accessible to a wider audience. Our platform offers a new way for artists and creators to monetize their work and showcase it to the world."

Crypto AI is also committed to sustainability and plans to use renewable energy sources for its operations. The project believes that it is essential to minimize the environmental impact of its operations and is actively exploring ways to reduce its carbon footprint.

"We understand the importance of sustainability, and we are committed to minimizing our environmental impact. We plan to use renewable energy sources for our operations and explore ways to reduce our carbon footprint," Weiner stated.

Crypto AI's launch is highly anticipated by the NFT community, and the project has already gained significant interest from artists and collectors worldwide. The project's innovative approach to NFT creation and its commitment to sustainability have made it stand out in a crowded marketplace.

About Crypto AI

Crypto AI ChatGPT Bot is an AI-powered bot that assists users in their conversations with automated and intelligent responses. We use natural language processing and machine learning algorithms to generate meaningful and relevant responses to user queries.

AI App on

https://cai.codes/artist

https://cai.codes/chat

Social Links

Twitter: https://twitter.com/CryptoAIbsc

Telegram: https://t.me/CryptoAI_eng

Medium: https://medium.com/@CryptoAI

Discord: https://github.com/crypto-ai-git

Media Contact

Brand: Crypto AI

E-mail: team@cai.codes

Website: https://cai.codes

SOURCE: Crypto AI

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Crypto AI Announces Its Launch, Using AI Machine Learning to ... - GlobeNewswire

March 22, 2023

2 min read

Chang J, et al. Machine learning-based investigation of prognostic indicators for oncologic outcome of pancreatic ductal adenocarcinoma. Presented at: Society of Surgical Oncology Annual Meeting; March 22-25, 2023; Boston.

Disclosures: Chang reports no relevant financial disclosures. One researcher reports funding from AngioDynamics, Checkmate Pharmaceuticals, Optimum Therapeutics and Regeneron for unrelated projects or clinical trials.

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Machine learning algorithms can help predict positive resection margin and lymph node metastases among patients with pancreatic ductal adenocarcinoma, according to study results.

The approach yielded greater positive predictive values than CT scan for both variables, findings presented at Society of Surgical Oncology Annual Meeting showed.

This hopefully can give providers the ability to identify patients with resectable pancreatic cancer who may benefit from neoadjuvant therapies, researcher Jeremy Chang, MD, MS, surgery resident at University of Iowa Hospitals, said during a press conference.

Pancreatic cancer is the third leading cause of cancer-related death, with a disproportionately high mortality rate compared with incidence due to most patients being diagnosed at advanced stages.

Approximately 15% to 20% of cases are deemed curable with surgery, according to study background. However, up to 80% of patients who undergo surgery develop local or distant recurrence, with key risk factors including lymph node metastasis, positive margins after surgery, larger tumor size and no receipt of chemotherapy.

A recent novel notion is there may be patients with resectable tumors at time of diagnosis who would actually benefit from neoadjuvant therapy or chemoradiation before surgery, Chang said. The question now is, how do we find who those patients are?

Chang and colleagues conducted a pilot study to assess the potential of machine learning which uses algorithms to learn and recognize patterns from input data to predict lymph node metastases or positive resection margins from preoperative scans.

Researchers used a 3-D convolutional neural network, optimized to process pixel or image data.

The network can be divided into three segments and 17 layers, Chang said. The first input layer consists of a CT image, followed by 12 layers of feature extraction, and then four layers of classification or output.

The cohort included adults diagnosed with pancreatic ductal adenocarcinoma who underwent pancreatectomy at University of Iowa Hospitals between 2015 and 2021. All patients had viable preoperative CT and postoperative pathology.

The analysis included 79 patients with a combined 480 CT images. The margin portion of the study also included 31 patients with unresectable locally advanced disease who served as positive controls.

Researchers divided patients into a training group which allowed the algorithm to learn and develop its pattern of recognition and a validation group.

The lymph node status portion of the study included a training group of 59 patients with a combined 340 images, and a validation group of 20 patients with a combined 140 images.

Results of a per-patient analysis showed a sensitivity of 100% (95% CI, 80-100) and specificity of 60% (95% CI, 23-93).

Researchers reported a prediction accuracy of 90%, a positive predictive value of 88% (95% CI, 66-88) and a negative predictive value of 100% (95% CI, 44-100).

The margin status portion of the study included a training group of 83 patients with a combined 629 images, as well as a validation group of 27 patients with a combined 252 images.

Results showed a prediction accuracy of 81%, a positive predictive value of 80% (95% CI, 64-98) and a negative predictive value of 82% (95% CI, 59-94).

For context, the positive predictive value of CT scans the most common modality for pancreatic cancer diagnosis and assessment is 73% for identifying positive nodes and 68% for determining whether resection margins will be positive, Chang said.

Future directions for this study will include increasing size of the training and testing cohorts to increase generalizability, Chang said. Were also planning to use this technology to develop a prospective clinical trial to help stratify patients for neoadjuvant treatment.

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Machine learning is no longer about experiments. Most industry-leading enterprises have already seen dramatic successes from their investments in machine learning (ML), and there is near-universal agreement among business executives that building data science capabilities is vital to maintaining and extending their competitive advantage.

The bullish outlook is evident in the U.S. Bureau of Labor Statistics predictions regarding growth of the data science career field: Employment of data scientists is projected to grow 36% from 2021 to 2031, much faster than the average for all occupations.

The aim now is to grow these initial successes beyond the specific parts of the business where they had initially emerged. Companies are looking to scale their data science capabilities to support their entire suite of business goals and embed ML-based processes and solutions everywhere the company does business.

Vanguards within the most data-centric industries, including pharmaceuticals, finance, insurance, aerospace and others, are investing heavily. They are assembling formidable teams of data scientists with varied backgrounds and expertise to develop and place ML models at the core of as many business processes as possible.

More often than not, they are running headlong into the challenges of executing data science projects across the regional, organizational, and technological divisions that abound in every organization. Data is worthless without the tools and infrastructure to use it, and both are fragmented across regions and business units, as well as in cloud and on-premises environments.

Even when analysts and data scientists overcome the hurdle of getting access to data in other parts of the business, they quickly find that they lack effective tools and hardware to leverage the data. At best, this results in low productivity, weeks of delays, and significantly higher costs due to suboptimal hardware, expensive data storage, and unnecessary data transfers. At worst, it results in project failure, or not being able to initiate the project to begin with.

Successful enterprises are learning to overcome these challenges by embracing hybrid-cloud strategies. Hybrid cloud the integrated use of on-premises and cloud environments also encompasses multicloud, the use of cloud offerings from multiple cloud providers. A hybrid-cloud approach enables companies to leverage the best of all worlds.

They can take advantage of the flexibility of cloud environments, the cost benefits of on-premises infrastructure, and the ability to select best-of-breed tools and services from any cloud vendor and machine learning operations tooling. More importantly for data science, hybrid cloud enables teams to leverage the end-to-end set of tools and infrastructure necessary to unlock data-driven value everywhere their data resides.

It allows them to arbitrage the inherent advantages of different environments while preserving data sovereignty and providing the flexibility to evolve as business and organizational conditions change.

While many organizations try to cope with disconnected platforms spread across different on-premises and cloud environments, today the most successful organizations understand that their data science operations must be hybrid cloud by design. That is, to implement end-to-end ML platforms that support hybrid cloud natively and provide integrated capabilities that work seamlessly and consistently across environments.

In a recent Forrester survey of AI infrastructure decision-makers, 71% of IT decision-makers say hybrid cloud support by their AI platform is important for executing their AI strategy, and 29% say its already critical. Further, 91% said they will be investing in hybrid cloud within two years, and 66% said they already had invested in hybrid support for AI workloads.

In addition to the overarching benefit of a hybrid-cloud strategy for data science the ability to execute data science projects and implement ML solutions anywhere in your business there are three key drivers that are accelerating the trend:

Data sovereignty: Regulatory requirements like GDPR are forcing companies to process data locally with the threat of heavy fines in more and more parts of the world. The EU Artificial Intelligence Act, which triages AI applications across three risk categories and calls for outright bans on applications deemed to be the riskiest, will go a step further than fines. Gartner predicts that 65% of the worlds population will soon be covered by similar regulations.

Cost optimization: The size of ML workloads grows as companies scale data science because of the increasing number of use cases, larger volumes of data and the use of computationally intensive, deep learning models. Hybrid-cloud platforms enable companies to direct workloads to the most cost-effective infrastructure; e.g., optimize utilization of an on-premise GPU cluster, and mitigate rising cloud costs.

Flexibility: Taking a hybrid-cloud approach allows for future-proofing to address the inevitable changes in business operations and IT strategy, such as a merger or acquisition involving a company that has a different tech stack, expansion to a new geography where your default cloud vendor does not operate or even a cloud vendor becoming a significant competitor.

Implementing a hybrid-cloud strategy for ML is easier said than done. For example, no public cloud vendor offers more than token support for on-premises workloads, let alone support for a competitors cloud, and the range of tools and infrastructure your data science teams need scales as you grow your data science rosters and undertake more ML projects. Here are the three essential capabilities for which every business must provide hybrid-cloud support in order to scale data science across the organization:

Full data science life cycle coverage: From model development to deployment to monitoring, enterprises need data science tooling and operations to manage every aspect of data science at scale.

Agnostic support for data science tooling: Given the variety of ML and AI projects and the differing skills and backgrounds of the data scientists across your distributed enterprise, your strategy needs to provide hybrid cloud support for the major open-source data science languages and frameworks and likely a few proprietary tools not to mention the extensibility to support the host of new tools and methods that are constantly being developed.

Scalable compute infrastructure: More data, more use cases and more advanced methods require the ability to scale up and scale out with distributed compute and GPU support, but this also requires an ability to support multiple distributed compute frameworks since no single framework is optimal for all workloads. Spark may work perfectly for data engineering, but you should expect that youll need a data-science-focused framework like Ray or Dask (or even OpenMPI) for your ML model training at scale.

Embedding ML models throughout your core business functions lies in the heart of AI-based digital transformation. Organizations must adopt a hybrid-cloud or equivalent multicloud strategy to expand beyond initial successes and deploy impactful ML solutions everywhere.

Data science teams need end-to-end, extensible and scalable hybrid-cloud ML platforms to access the tools, infrastructure and data they need to develop and deploy ML solutions across the business. Organizations need these platforms for the regulatory, cost and flexibility benefits they provide.

The Forrester survey notes that organizations that adopt hybrid cloud approaches to AI development are already seeing the benefits across the entire AI/ML life cycle, experiencing 48% fewer challenges in deploying and scaling their models than companies relying on a single cloud strategy. All evidence suggests that the vanguard of companies who have already invested in their data science teams and platforms are pulling even further ahead using hybrid cloud.

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Unlock the Next Wave of Machine Learning with the Hybrid Cloud - The New Stack

With chatbots like ChatGPT making a splash, machine learning is playing an increasingly prominent role in our lives. For many of us, its been a mixed bag. We rejoice when our Spotify For You playlist finds us a new jam, but groan as we scroll through a slew of targeted ads on our Instagram feeds.

Machine learning is also changing many fields that may seem surprising. One example is my discipline, ornithology the study of birds. It isnt just solving some of the biggest challenges associated with studying bird migration; more broadly, machine learning is expanding the ways in which people engage with birds. As spring migration picks up, heres a look at how machine learning is influencing ways to research birds and, ultimately, to protect them.

Most birds in the Western Hemisphere migrate twice a year, flying over entire continents between their breeding and nonbreeding grounds. While these journeys are awe-inspiring, they expose birds to many hazards en route, including extreme weather, food shortages and light pollution that can attract birds and cause them to collide with buildings.

Our ability to protect migratory birds is only as good as the science that tells us where they go. And that science has come a long way.

In 1920, the U.S. Geological Survey launched the Bird Banding Laboratory, spearheading an effort to put bands with unique markers on birds, then recapture the birds in new places to figure out where they traveled. Today researchers can deploy a variety of lightweight tracking tags on birds to discover their migration routes. These tools have uncovered the spatial patterns of where and when birds of many species migrate.

However, tracking birds has limitations. For one thing, over 4 billion birds migrate across the continent every year. Even with increasingly affordable equipment, the number of birds that we track is a drop in the bucket. And even within a species, migratory behavior may vary across sexes or populations.

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Further, tracking data tells us where birds have been, but it doesnt necessarily tell us where theyre going. Migration is dynamic, and the climates and landscapes that birds fly through are constantly changing. That means its crucial to be able to predict their movements.

This is where machine learning comes in. Machine learning is a subfield of artificial intelligence that gives computers the ability to learn tasks or associations without explicitly being programmed. We use it to train algorithms that tackle various tasks, from forecasting weather to predicting March Madness upsets.

But applying machine learning requires data and the more data the better. Luckily, scientists have inadvertently compiled decades of data on migrating birds through the Next Generation Weather Radar system. This network, known as NEXRAD, is used to measure weather dynamics and help predict future weather events, but it also picks up signals from birds as they fly through the atmosphere.

BirdCast is a collaborative project of Colorado State University, the Cornell Lab of Ornithology and the University of Massachusetts that seeks to leverage that data to quantify bird migration. Machine learning is central to its operations. Researchers have known since the 1940s that birds show up on weather radar, but to make that data useful, we need to remove nonavian clutter and identify which scans contain bird movement.

This process would be painstaking by hand but by training algorithms to identify bird activity, we have automated this process and unlocked decades of migration data. And machine learning allows the BirdCast team to take things further: By training an algorithm to learn what atmospheric conditions are associated with migration, we can use predicted conditions to produce forecasts of migration across the continental U.S.

BirdCast began broadcasting these forecasts in 2018 and has become a popular tool in the birding community. Many users may recognize that radar data helps produce these forecasts, but fewer realize that its a product of machine learning.

Currently these forecasts cant tell us what species are in the air, but that could be changing. Last year, researchers at the Cornell Lab of Ornithology published an automated system that uses machine learning to detect and identify nocturnal flight calls. These are species-specific calls that birds make while migrating. Integrating this approach with BirdCast could give us a more complete picture of migration.

These advancements exemplify how effective machine learning can be when guided by expertise in the field where it is being applied. As a doctoral student, I joined Colorado State Universitys Aeroecology Lab with a strong ornithology background but no machine learning experience. Conversely, Ali Khalighifar, a postdoctoral researcher in our lab, has a background in machine learning but has never taken an ornithology class.

Together, we are working to enhance the models that make BirdCast run, often leaning on each others insights to move the project forward. Our collaboration typifies the convergence that allows us to use machine learning effectively.

Machine learning is also helping scientists engage the public in conservation. For example, forecasts produced by the BirdCast team are often used to inform Lights Out campaigns.

These initiatives seek to reduce artificial light from cities, which attracts migrating birds and increases their chances of colliding with human-built structures, such as buildings and communication towers. Lights Out campaigns can mobilize people to help protect birds at the flip of a switch.

As another example, the Merlin bird identification app seeks to create technology that makes birding easier for everyone. In 2021, the Merlin staff released a feature that automates song and call identification, allowing users to identify what theyre hearing in real time, like an ornithological version of Shazam.

This feature has opened the door for millions of people to engage with their natural spaces in a new way. Machine learning is a big part of what made it possible.

Sound ID is our biggest success in terms of replicating the magical experience of going birding with a skilled naturalist, Grant Van Horn, a staff researcher at the Cornell Lab of Ornithology who helped develop the algorithm behind this feature, told me.

Opportunities for applying machine learning in ornithology will only increase. As billions of birds migrate over North America to their breeding grounds this spring, people will engage with these flights in new ways, thanks to projects like BirdCast and Merlin. But that engagement is reciprocal: The data that birders collect will open new opportunities for applying machine learning.

Computers cant do this work themselves. Any successful machine learning project has a huge human component to it. That is the reason these projects are succeeding, Van Horn said to me.

This article is republished from The Conversation, an independent nonprofit news site dedicated to sharing ideas from academic experts. Like this article? Subscribe to our weekly newsletter.

It was written by: Miguel Jimenez, Colorado State University.

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Miguel Jimenez receives funding from the National Aeronautics and Space Administration.

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Scientists are using machine learning to forecast bird migration and identify birds in flight by their calls - Yahoo News