In this "On the Job" segment from Cheddar News, Amin Venjara, General Manager of Data Solutions at ADP, describes the importance of data and how human resources leaders are relying on real-time access to data now more than ever. Venjara offers real-world examples of data's impact on the top challenges faced by organizations today.

Businesses big and small have been utilizing the latest tech and innovation to make the new remote and hybrid working environments possible.

Speaking with Cheddar News, above, Amin Venjara (AV), says relying on quality and accessible data to take action is how today's HR teams are impacting the modern workforce.

Q: How does data influence the role of human resources (HR)?

AV: The last few years have thrust HR teams into the spotlight. Think about all the changes we've seen managing the onset of the pandemic, the return to the workplace, the great resignation and all the challenges that's brought and even the increased focus on diversity, equity and inclusion. HR has been at the focal point of responding to these challenges. And in response, we've seen an uptick in the use of workforce analytics and benchmarking. HR teams need the data to be able to help make decisions in real time as things are changing. And they're using it with the executives and managers they support to make data-driven decisions.

Q: Clearly data-driven solutions are critical in today's workforce as you've been discussing, where has data made the most significant impact?

AV: When we talk to employers, we continuously hear about four key areas related to their workforce: attracting top talent, retaining and engaging talent, optimizing labor costs, and fostering a diverse, equitable and inclusive workforce.

To give an example of the kind of impact that data can have. We have a product that helps organizations calculate and take action on pay equity. They can see gaps by gender and race ethnicity and based on internal and market data. Over 70% of active clients using this tool are seeing a decrease in pay equity gaps. If you look at the size of this - they're spending over a billion dollars to close those gaps. That's not just analytics and data - that's taking action. So, think about the impact that has on the message about equal pay for equal work. And also, the impact it has on productivity, and the lives of those individual workers and their families.

Q: In today's tight talent market, employers increasingly need help recruiting and even retaining workers. How can data and machine learning alleviate some of those very pressing challenges?

AV: Here's an interesting thing about what's happening in the current labor market. U.S. employment numbers are back to pre-pandemic levels with 150 million workers on the payroll. However, we're at the lowest unemployed workers to jobs openings rate we've seen in over 15 years. To put it simply, it's a candidate's market out there; and jobs are chasing workers.

Two things to keep in mind: employers have to employ data-driven strategies to be competitive. So we're seeing with labor markets changing, remote work, hybrid work, expectations on pay and even the physical locations of workers people have moved a lot. Employers need access to real-time data, accurate data on supply and demand of labor and on compensation to hire the right workers and keep the ones they have.

The second thing is really about the adoption of machine learning in recruiting workflows. We're seeing machine learning being adopted in chatbots for personalizing the experience and even helping with scheduling, but also AI-based algorithms to help score candidate profiles against jobs. Overall, the best organizations are combing technology and data with their recruiting and hiring managers to decrease the overall time to fill open jobs.

Q: Becoming data confident might be a concern or even perhaps intimidating for some, but what's an example of how an organization can use data well?

AV: A lot of organizations are trying to make this happen. We recently worked with a quick service restaurant with about 2,000 locations across the U.S. In light of the supply chain challenges and demographic shifts of the last couple of years, they wanted to know how to combine and optimize the supply at each location based on expected demand.

Their research enabled them to correlate demographics, things like age, income and even family status to items on the menu like salads, sandwiches and kids' meals. But what they needed was a stronger signal on what's happening in the local context of each location. They had used internal data for so long, but things had shifted. By using our monthly anonymized and aggregated data from nearly 20% of the workforce, they were able to optimize their demand forecasting models and increase their supply chain efficiency. There are two lessons to think about. They had a key strategic problem, and they worked backwards from that. That's a key piece of becoming data confident - focusing on something that matters and making a data-driven decisions about it. The second is about going beyond the four walls of your organization. There are so many different and new sources of data available due to the digitization of our economy. In order to lock the insight and the strength of signal you need you really need to look for the best sources to get there.

Q: How do you see the role of data evolving as we look toward the future of work?

AV: Data has really come the language of business right now. I see a couple of trends as we look out. The first is the acceleration of data in the flow of work. When you look at a lot of organizations today, when people need data, they have to go to a reporting group or a business intelligence group to request the data. Then it takes a couple cycles to get it right and then make a decision. The cycle time can be high.

What I expect to see now is data more and more in the flow of work where business decision makers are working immediately; they have the right data at their fingertips. You see that across domains. Second is just the separation between haves and have nots. With the increasing speed of change, data haves are going to be able to outstrip data have nots. Those who have invested in building the right organizational, technical, and cultural muscle will see the spoils of this in the years to come.

Learn more

In the post-pandemic world of work, the organizations that prioritize people first will rise to the top. Find out how to make HR more personalized to adapt to today's changing talent landscape. Get our guide: Work is personal

Tags: Compensation Diversity and Inclusion Trends and Innovation Salary and Wages Technology HCM Technology HR Recruiting and Hiring Articles

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How Data Has Changed the World of HR - ADP

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The era of Big Data has helped democratize information, creating a wealth of data and growing revenues at technology-based companies. But for all this intelligence, were not getting the level of insight from the field of machine learning that one might expect, as many companies struggle to make machine learning (ML) projects actionable and useful. A successful AI/ML program doesnt start with a big team of data scientists. It starts with strong data infrastructure. Data needs to be accessible across systems and ready for analysis so data scientists can quickly draw comparisons and deliver business results, and the data needs to be reliable, which points to the challenge many companies face when starting a data science program.

The problem is that many companies jump feet first into data science, hire expensive data scientists, and then discover they dont have the tools or infrastructure data scientists need to succeed. Highly-paid researchers end up spending time categorizing, validating and preparing data instead of searching for insights. This infrastructure work is important, but also misses the opportunity for data scientists to utilize their most useful skills in a way that adds the most value.

When leaders evaluate the reasons for success or failure of a data science project (and 87% of projects never make it to production) they often discover their company tried to jump ahead to the results without building a foundation of reliable data. If they dont have that solid foundation, data engineers can spend up to 44% of their time maintaining data pipelines with changes to APIs or data structures. Creating an automated process of integrating data can give engineers time back, and ensure companies have all the data they need for accurate machine learning. This also helps cut costs and maximize efficiency as companies build their data science capabilities.

Machine learning is finicky if there are gaps in the data, or it isnt formatted properly, machine learning either fails to function, or worse, gives inaccurate results.

When companies get into a position of uncertainty about their data, most organizations ask the data science team to manually label the data set as part of supervised machine learning, but this is a time-intensive process that brings additional risks to the project. Worse, when the training examples are trimmed too far because of data issues, theres the chance that the narrow scope will mean the ML model can only tell us what we already know.

The solution is to ensure the team can draw from a comprehensive, central store of data, encompassing a wide variety of sources and providing a shared understanding of the data. This improves the potential ROI from the ML models by providing more consistent data to work with. A data science program can only evolve if its based on reliable, consistent data, and an understanding of the confidence bar for results.

One of the biggest challenges to a successful data science program is balancing the volume and value of the data when making a prediction. A social media company that analyzes billions of interactions each day can use the large volume of relatively low-value actions (e.g. someone swiping up or sharing an article) to make reliable predictions. If an organization is trying to identify which customers are likely to renew a contract at the end of the year, then its likely working with smaller data sets with large consequences. Since it could take a year to find out if the recommended actions resulted in success, this creates massive limitations for a data science program.

In these situations, companies need to break down internal data silos to combine all the data they have to drive the best recommendations. This may include zero-party information captured with gated content, first-party website data, and data from customer interactions with the product, along with successful outcomes, support tickets, customer satisfaction surveys, even unstructured data like user feedback. All of these sources of data contain clues if a customer will renew their contract. By combining data silos across business groups, metrics can be standardized, and theres enough depth and breadth to create confident predictions.

To avoid the trap of diminishing confidence and returns from an ML/AI program, companies can take the following steps.

By building the right infrastructure for data science, companies can see whats important for the business, and where the blind spots are. Doing the groundwork first can deliver solid ROI, but more importantly, it will set up the data science team up for significant impact. Getting a budget for a flashy data science program is relatively easy, but remember, the majority of such projects fail. Its not as easy to get budget for the boring infrastructure tasks, but data management creates the foundation for data scientists to deliver the most meaningful impact on the business.

AlexanderLovell is head of product atFivetran.

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How to make the most of your AI/ML investments: Start with your data infrastructure - VentureBeat

Engineering | News releases | Research | Technology

July 11, 2022

ClearBuds use a novel microphone system and are one of the first machine-learning systems to operate in real time and run on a smartphone.Raymond Smith/University of Washington

As meetings shifted online during the COVID-19 lockdown, many people found that chattering roommates, garbage trucks and other loud sounds disrupted important conversations.

This experience inspired three University of Washington researchers, who were roommates during the pandemic, to develop better earbuds. To enhance the speakers voice and reduce background noise, ClearBuds use a novel microphone system and one of the first machine-learning systems to operate in real time and run on a smartphone.

The researchers presented this project June 30 at the ACM International Conference on Mobile Systems, Applications, and Services.

ClearBuds differentiate themselves from other wireless earbuds in two key ways, said co-lead author Maruchi Kim, a doctoral student in the Paul G. Allen School of Computer Science & Engineering. First, ClearBuds use a dual microphone array. Microphones in each earbud create two synchronized audio streams that provide information and allow us to spatially separate sounds coming from different directions with higher resolution. Second, the lightweight neural network further enhances the speakers voice.

While most commercial earbuds also have microphones on each earbud, only one earbud is actively sending audio to a phone at a time. With ClearBuds, each earbud sends a stream of audio to the phone. The researchers designed Bluetooth networking protocols to allow these streams to be synchronized within 70 microseconds of each other.

The teams neural network algorithm runs on the phone to process the audio streams. First it suppresses any non-voice sounds. And then it isolates and enhances any noise thats coming in at the same time from both earbuds the speakers voice.

Because the speakers voice is close by and approximately equidistant from the two earbuds, the neural network can be trained to focus on just their speech and eliminate background sounds, including other voices, said co-lead author Ishan Chatterjee, a doctoral student in the Allen School. This method is quite similar to how your own ears work. They use the time difference between sounds coming to your left and right ears to determine from which direction a sound came from.

Shown here, the ClearBuds hardware (round disk) in front of the 3D printed earbud enclosures.Raymond Smith/University of Washington

When the researchers compared ClearBuds with Apple AirPods Pro, ClearBuds performed better, achieving a higher signal-to-distortion ratio across all tests.

Its extraordinary when you consider the fact that our neural network has to run in less than 20 milliseconds on an iPhone that has a fraction of the computing power compared to a large commercial graphics card, which is typically used to run neural networks, said co-lead author Vivek Jayaram, a doctoral student in the Allen School. Thats part of the challenge we had to address in this paper: How do we take a traditional neural network and reduce its size while preserving the quality of the output?

The team also tested ClearBuds in the wild, by recording eight people reading from Project Gutenberg in noisy environments, such as a coffee shop or on a busy street. The researchers then had 37 people rate 10- to 60-second clips of these recordings. Participants rated clips that were processed through ClearBuds neural network as having the best noise suppression and the best overall listening experience.

One limitation of ClearBuds is that people have to wear both earbuds to get the noise suppression experience, the researchers said.

But the real-time communication system developed here can be useful for a variety of other applications, the team said, including smart-home speakers, tracking robot locations or search and rescue missions.

The team is currently working on making the neural network algorithms even more efficient so that they can run on the earbuds.

Additional co-authors are Ira Kemelmacher-Shlizerman, an associate professor in the Allen School; Shwetak Patel, a professor in both the Allen School and the electrical and computer engineering department; and Shyam Gollakota and Steven Seitz, both professors in the Allen School. This research was funded by The National Science Foundation and the University of Washingtons Reality Lab.

For more information, contact the team at clearbuds@cs.washington.edu.

Read more:
ClearBuds: First wireless earbuds that clear up calls using deep learning - University of Washington

A research team led by Professor Sanguk Kim (Department of Life Sciences) at POSTECH is gaining attention as they have improved the accuracy of predicting patient response to immune checkpoint inhibitors (ICIs) by using network-based machine learning.

The research team discovered new network-based biomarkers by analyzing the clinical results of more than 700 patients with three different cancers (melanoma, gastric cancer, and bladder cancer) and the transcriptome data of the patients' cancer tissues. By utilizing the network-based biomarkers, the team successfully developed artificial intelligence that could predict the response to anticancer treatment.

The team further proved that the treatment response prediction based on the newly discovered biomarkers was superior to that based on conventional anticancer treatment biomarkers including immunotherapy targets and tumor microenvironment markers.

In their previous study, the research team had developed machine learning that could predict drug responses to chemotherapy in patients with gastric or bladder cancer. This study has shown that artificial intelligence using the interactions between genes in a biological network could successfully predict the patient response to not only chemotherapy, but also immunotherapy in multiple cancer types.

This study helps detect patients who will respond to immunotherapy in advance and establish treatment plans, resulting in customized precision medicine with more patients to benefit from cancer treatments. Supported by the POSTECH Medical Device Innovation Center, the Graduate School of Artificial Intelligence, and ImmunoBiome Inc, this study was recently published inNature Communications, an international peer-reviewed journal.

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Using Machine Learning to Predict Immunotherapy Response - Medical Device and Diagnostics Industry

REDWOOD CITY, Calif.--(BUSINESS WIRE)--C3 AI (NYSE: AI), the Enterprise AI application software company, today announced that Forrester Research has named it a Leader in AI and Machine Learning Platforms in its July 2022 report, The Forrester Wave: AI/ML Platforms, Q3 2022.

Ahead of its time, C3 AIs strategy is to make AI application-centric by building a growing library of industry solutions, forging deep industry partnerships, running in every cloud, and facilitating extreme reuse through common data models, the report states.

We are pleased to be recognized as a leader in AI and ML platforms," said Thomas Siebel, C3 AI CEO. Im delighted to see C3 AIs significant investments in enterprise AI software be acknowledged. I believe that Forrester Research has made an important contribution, having published the first professional comprehensive analysis of enterprise AI and Machine Learning platforms, Siebel continued, changing the dialogue from a focus on disjointed tools to the importance of cohesive enterprise AI platforms. This is certain to accelerate the market adoption of enterprise AI and simplify often protracted decision processes.

Of the 15 vendors in the report, C3 AI received the top ranking in the Strategy category. For the following criteria, C3 AI received:

Download The Forrester Wave: AI and Machine Learning Platforms, Q3 2022 report here.

About C3 AI

C3 AI is the Enterprise AI application software company. C3 AI delivers a family of fully integrated products including the C3 AI Suite, an end-to-end platform for developing, deploying, and operating enterprise AI applications and C3 AI Applications, a portfolio of industry-specific SaaS enterprise AI applications that enable the digital transformation of organizations globally.

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C3 AI Named a Leader in AI and Machine Learning Platforms - Business Wire

SAN FRANCISCO, July 12, 2022 (GLOBE NEWSWIRE) -- Tecton, the leading ML feature platform company, today announced record demand for its platform and Feast, the most popular open source feature store:

"We believe that any company should be able to develop reliable operational ML applications and easily adopt real-time capabilities no matter the use case at hand or the engineering resources on staff. This new funding will help us further build and strengthen both Tectons feature platform for ML and the Feast open source feature store, enabling organizations of all sizes to build and deploy automated MLinto live, customer-facing applications and business processes, quickly and at scale, said Mike Del Balso, co-founder and CEO of Tecton.

Tecton was founded by the creators of Ubers Michelangelo platform to make world-class ML accessible to every company. Tecton is a fully-managed ML feature platform that orchestrates the complete lifecycle of features, from transformation to online serving, with enterprise-grade SLAs. The platform enables ML engineers and data scientists to automate the transformation of raw data, generate training data sets and serve features for online inference at scale. Whether organizations are building batch pipelines or already including real-time features in their ML initiatives, Tecton solves the many data and engineering hurdles that keep development times painfully high and, in many cases, preventing predictive applications from ever reaching production at all.

4 Components of Tectons Feature Platform

Major Company Milestones2020:

2021:

2022:

Tecton Raises $100 Million in Series C FundingToday Tecton also announced that it has raised $100 million in Series C funding bringing the total raised to $160 million. This round was led by new investor Kleiner Perkins with participation from strategic investors Databricks and Snowflake Ventures, previous investors Andreessen Horowitz and Sequoia Capital and new investors Bain Capital Ventures and Tiger Global. Tecton plans to use the money to further deliver on customer value and to scale both engineering and go-to-market teams.

We expect the software we use today to be highly personalized and intelligent. While ML makes this possible, it remains far from reality as the enabling infrastructure is prohibitively difficult to build for all but the most advanced companies, said Bucky Moore, partner, Kleiner Perkins. Tecton makes this infrastructure accessible to any team, enabling them to build ML apps faster. As this continues to accelerate their growth trajectory, we are proud to partner with Mike, Kevin and team to pioneer and lead this exciting new space.

The investment in Tecton is a natural fit for Databricks Ventures as we look to extend the lakehouse ecosystem with best-in-class solutions and support companies that align with our mission to simplify and democratize data and AI, said Andrew Ferguson, Head of Databricks Ventures. We're excited to deepen our partnership with the Tecton team and look forward to delivering continued innovation for our joint customers.

Together, Tecton and Snowflake enable data teams to securely and reliably store, process and manage the complete lifecycle of ML features for production in Snowflake, making it easier for users across data science, engineering and analyst teams to collaborate and work from a single source of data truth, said Stefan Williams, VP Corporate Development and Snowflake Ventures at Snowflake. This investment expands our partnership and is the latest example of Snowflakes commitment to helping our customers effortlessly get the most value from their data.

Additional Resources

About TectonTectons mission is to make world-class ML accessible to every company. Tectons feature platform for ML enables data scientists to turn raw data into production-ready features, the predictive signals that feed ML models. The founders created the Uber Michelangelo ML platform, and the team has extensive experience building data systems for industry leaders like Google, Facebook, Airbnb and Uber. Tecton is backed by Andreessen Horowitz, Bain Capital Ventures, Kleiner Perkins, Sequoia Capital and Tiger Global as well as by strategic investors Databricks and Snowflake Ventures. Tecton is the main contributor and committer of Feast, the leading open source feature store. For more information, visit https://www.tecton.ai or follow @tectonAI.

Media and Analyst Contact:Amber Rowlandamber@therowlandagency.com+1-650-814-4560

[1] Gartner, Cool Vendors in Enterprise AI Operationalization and Engineering, Chirag Dekate, Farhan Choudhary, Soyeb Barot, Erick Brethenoux, Arun Chandrasekaran, Robert Thanaraj, Georgia O'Callaghan, 27 April 2021 (report available to Gartner subscribers here: https://www.gartner.com/doc/4001037)

Gartner Disclaimer: Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartners research organization and should not be construed as statements of fact. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/3b873f04-a539-44c0-8a98-69cde58f42ad

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Tecton Reports Record Demand for Its Machine Learning Feature Platform as It Raises $100 Million in Funding Led by Kleiner Perkins With Participation...

How Is the Healthcare Sector Being Revolutionized by Machine Learning?

Machine learning is set to change the healthcare sector. What if you were informed that machines would soon carry out surgery? Yes, machine learning has improved quickly to the point that it may soon be possible to execute medical procedures with little to no assistance from a doctor. By 2022, machine learning will be employed extensively in the healthcare sector.

The first thing that springs to mind when one hears the words artificial intelligence or machine learning is robots, but machine learning is far more involved than that. Machine learning has advanced in every conceivable industry and transformed numerous businesses, including finance, retail, and healthcare. This article will discuss how machine learning changes the healthcare sector. So lets get down to business right away.

With machine learning and artificial intelligence application, a system can learn from its mistakes and improve over time. Themain aim is to enable computers to learn autonomously, without any help from human input. Data observations, pattern discovery, and future decision-making are the first steps in the learning process. In India, machine learning has begun to take hold.

A fundamental component of artificial intelligence is machine learning, which enables computers to learn from the past and predict the future.

Data exploration and pattern matching are involved, with little assistance from humans. Machine learning has mainly been used in four technologies:

1. Supervised Learning:

A machine learning technique called supervised learning necessitates monitoring as a student-teacher interaction does. In supervised learning, a machine is trained using data that has already been correctly labelled with some of the outputs. As a result, supervised learning algorithms assess sample data whenever new data is entered into the system and use that labelled data to predict accurate results.

It is classified into two different categories of algorithms:

Because of technology, individuals are able to collect or produce data based on experience.Utilising certain labelled data points from the training set, it operates similarly to how people learn. It assists in addressing various challenging computational issues and optimising the performance of models utilising experience.

2. Unsupervised Learning:

Unlike supervised learning, unsupervised learning allowsa machine to be trained without needing to classify or clearly label data. Even without any labelled training data, it seeks to create groups of unsorted data based on some patterns and differences. Since there is no supervision in unsupervisedlearning, the machines are not given any sample data. As a result, robots can only discover hidden structures in unlabeled data.

Combining supervised and unsupervised learning techniquesis known as semi-supervised learning. It is used to get around both supervised and unsupervised learnings shortcomings.

In the semi-supervised learning approach, both labelled and unlabelled data are used to train the machine. Nevertheless, it includes a sizable number of unlabeled cases and a few examples with labels. Some of the most well-known semi-supervised learning real-world applications are speech analysis, web content classification, protein sequence classification, and text document classifiers.

A feedback-based machine learning technique known asreinforcement learning excludes the need for labelled data. An agent learns howto behave, thereby performing actions and observing how they affect theenvironment. Agents can offer compliments for each constructive action andcriticism for destructive ones. Since there are no training data forreinforcement learning, agents can only learn from their experience.

Even though constantly new technologies are emerging,machine learning is still utilised in several different industries.

Machine learning is essential because it helps companiescreate new products and gives them a picture of consumer behaviour trends andoperational business patterns.

Machine learning is fundamental to theoperations of many of the leading businesses of today, like Facebook, Google,and Uber. Machine learning has become a major point of competitive differencefor many firms.

Machine learning has a number of real-world uses that produce tangible business outcomes, including time and money savings, that could significantly impact your companys future. One mainly observes a significant impact on the customer care sector, where machine learning enables humans to complete tasks more quickly and effectively. Through Virtual Assistant solutions, machine learning automates actions that would usually require a human person to complete them, including resetting a password or checking an accounts balance. By doing this, valuable agent time is freed up so they can concentrate on the high-touch, complex decision-making tasks that humans excel at but that machines struggle with.

There have been many breakthroughs in the healthcaresector, but machine learning has improved the efficiency of healthcare firms.

Although machine learning has come a long way, a doctors brain remains thebest machine learning tool in the healthcare sector. Many doctors are concernedthat machine learning will take over the healthcare sector.

The focus should be placed on how doctors may utilisemachine learning as a tool to enhance clinical use and supplement patientcare. Even if machine learning completely replaces doctors, patients would still require a human touch and attentive care.

Machine learning is making inroads into severalbusinesses, and this trend appears to continue indefinitely. Additionally, ithas begun to demonstrate its abilities in the healthcare sector. Some of theways it is used there are:

Machine learning algorithms that forecast disease orenable early disease and illness diagnoses are already under development byscientists. Artificial intelligence algorithms are being developed by thetechnological startup feebris, based in the UK, to identify complicatedrespiratory disorders accurately. The Computer Science and Artificial Intelligence Lab at MIT have created a novel deep learning-based prediction model that can forecast the onset of breast cancer up to five years in thefuture.

Since it started in 1980, the use of robotics inhealthcare has been expanding quickly. Although many people still find the ideaof a robot performing surgery unsettling, it will soon become a normal practice.

In hospitals, robotics is also utilised to monitor the patients and notify thenurses when human interaction is necessary.

The robotic assistant can find the blood vessel and take the patients blood with minimal discomfort and concern. In pharmaceuticallabs, robots also dispense and prepare drugs and vaccinations. Robotic carts are utilised in large facilities to transport medical supplies. Speaking abouthumans being replaced by robots, that wont be happening anytime soon; roboticscan only help doctors, never taking their place.

The procedure or technique known as medical imagingdiagnostic involves creating a visual depiction of tissue or internal organparts to monitor health, diagnose, and treat disorders. Additionally, it aidsin the creation of an anatomy and physiology database. Using medical imagingtechnology like ultrasound and MRI can prevent the need for surgical procedures.

Machine learning algorithms are properly taught torecognise the subtleties in CT scans and MRIs and can handle enormous amountsof medical pictures quickly. A deep learning team has developed an algorithmfrom the US, France, and Germany that can diagnose skin cancer more preciselythan a dermatologist.

Because of its advantages, machine learning is becomingincreasingly popular among healthcare organisations. Several advantages include:

The ability of machine learning to precisely recognisepatterns and data, which may be impossible for a human to do, is one of itsgreatest strengths. It can quickly and efficiently process enormous amounts of dataand patterns. All of these are feasible with the new invention.

Because maintaining health data requires a lot of work,machine learning is used to streamline the procedure and reduce the time andeffort needed. Machine learning is developing cutting-edge technology forkeeping smart data records in the modern world.

By gaining knowledge from patterns and data over time,machine learning adapts. The main advantage of machine learning is that it caneasily execute procedures and requires little human interaction.

There is also some uncertainty because, despite itsadvantages, machine learning also has drawbacks. Among them are:

Machine learning trains its algorithms using enormousdata sets and patterns since it adapts through patterns and data settings. Theinformation must be accurate and of high calibre.

For machine learning to produce correct results, it needsenough time for its algorithms to absorb and adjust to the patterns and data.It functions better with more computing power.

Machine learning is extremely error-prone, necessitates avast quantity of data, and may not perform as intended if not given enough ofit. Any inaccurate data fed to the machine may result in an undesirable result.

The advancement of machine learning will enable theautomatic early detection of most ailments. It will also improve the efficiencyand accuracy of disease detection to lessen the strain on doctors. Futurehealthcare will change thanks to AI and machine learning.

Machine learning has grown quickly in every industry,including navigation, business, retail, and banking. However, success in thehealthcare sector is challenging due to the scarcity of high-calibre scientistsand the limited availability of data. Numerous elements, including machine learning, still need to be addressed.

The use of machine learning in the healthcare sector hasincreased in popularity and usage. By simplifying their tasks, ML benefitspatients and physicians in various ways. Automating medical billing, offeringclinical decision support, and creating clinical care standards are some of themost popular uses of machine learning. Machine learning has numerousapplications that are currently being investigated and developed.

Futuredevelopments in machine learning (ML) applications in the healthcare industry will greatly improve the quality of life for people.

Edited by Prakriti Arora

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How Is The Healthcare Sector Being Revolutionized By Machine Learning 2022 - Inventiva

On an early visit to the University of South Carolina, Amit Sheth was surprised when 10 deans showed up for a meeting with him about artificial intelligence.

Sheth the incoming director of the universitys Artificial Intelligence Institute at the time thought he would need to sell the deans on the idea. Instead, it was them pitching the importance of artificial intelligence to him.

All of them were telling me why they are interested in AI, rather than me telling them why they should be interested in AI, Sheth said in a 2020 interview with the universitys Breakthrough research magazine. The awareness of AI was already there and the desire to incorporate AI into the activities that their faculty and students do was already on the campus.

Since the university announced the institute in 2019, that interest has only grown. There are now dozens of researchers throughout campus exploring how artificial intelligence and machine learning can be used to advance fields from health care and education to manufacturing and transportation. On Oct. 6, faculty will gather at the Darla Moore School of Business for a panel discussion on artificial intelligence led by Julius Fridriksson, vice president for research.

South Carolina's efforts stand out in several ways: the collaborative nature of research, which involves researchers from many different colleges and schools; a commitment to harnessing the power of AI in an ethical way; and the university's commitment to projects that will have a direct, real-world impact.

This week, as the Southeastern Conference marks AI in the SEC Day, we look at some of the remarkable efforts of South Carolina researchers in the area of artificial intelligence.

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Harnessing the power of artificial intelligence - UofSC News & Events - SC.edu

Startups are born in any number of places: offices, homes, restaurants, planes, trains and automobiles. But in the case of Charles Simons Future AI, a DC-based AI company, it was born on a boat.

After sailing all around the world, Simon said that he was parked in the Annapolis, Maryland yacht base in 2018 when he began writing his companys initial software. That eventually became a product called Brain Simulator, an open-source tool used by people the world over.

A few years later, in January 2022, Simon and cofounder Andre Slabber raised $2 million in January of 2022 to launch Future AI, an artificial intelligence company focused on general intelligence. In the time since, the company has grown to 20 employees, most of whom are developers, and is about to begin its beta launch.

General intelligence, according to Simon, is the sect of AI that focuses on emulating what neurons can do in a human brain. Most machine learning-concerned AI focuses on finding and analyzing data and correlations, which is useful to several industries, but the algorithms lack some of the basic human brain functions that we use every day.

It doesnt know the things that you and I take for granted, Simon told Technical.ly. We know that things exist in reality, we know that there is a reality or, at least, we assume theres a reality because it sure looks like it. The machine learning direction is simply not targeted to go there.

Those functions include things like knowing cause and effect, i.e. knowing your impact on reality and the future. Future AI is working on developing software that understands multi-sensory input as well as a data structure that can support any kind of information. It additionally is building an internal mental model, which replicates how human brains are generally aware of whats around them, even if theyre not directly looking at something (need an example? Picture the wall thats likely behind you without looking at it).

Its Sallie product, which just launched, attempts to make those brain functions a reality through AI. Sallie is a small pod that features a camera, computer, body and sensory technology to explore, talk, hear and touch things. Sallie, Simon emphasized, is still in the works; While she has the beginnings of those abilities, she largely exists as a toy or entertainment at the moment.

The software officially launched Friday, and Future AI is building a sign-up list for beta testing that is set to begin in the fourth quarter. He hopes to ramp up development in 2023, and ultimately expects to have a project with more complete general intelligence in four to five years.

Still, Simon stressed that the technology is still very new. It in no way interrupts the work of machine learning but instead adds new capabilities. And as a former electrical engineer who developed different machine learning-based software for neurological systems that hed love to keep in the machine learning sphere, he would know.

Artificial general intelligence has been a holy grail of computer science since the 1950s and when it exists, it will permeate and revolutionize all of computing, Simon said.

Read more:
DC's Future AI is working on the 'holy grail' of artificial intelligence - Technical.ly

ITHACA, N.Y. --Employing artificial intelligence to help improve outcomes for people with cardiovascular disease is the focus of a three-year, $15 million collaboration among Cornell Tech, the Cornell Ann S. Bowers College of Computing and Information Science (Cornell Bowers CIS) and NewYork-Presbyterian with physicians from its affiliated medical schools Weill Cornell Medicine and Columbia University Vagelos College of Physicians and Surgeons (Columbia University VP&S).

The Cardiovascular AI Initiative, to be funded by NewYork-Presbyterian, was launched this summer in a virtual meeting featuring approximately 40 representatives from the institutions.

AI is poised to fundamentally transform outcomes in cardiovascular health care by providing doctors with better models for diagnosis and risk prediction in heart disease, said Kavita Bala, professor of computer science and dean of Cornell Bowers CIS. This unique collaboration between Cornells world-leading experts in machine learning and AI and outstanding cardiologists and clinicians from NewYork-Presbyterian, Weill Cornell Medicine and Columbia will drive this next wave of innovation for long-lasting impact on cardiovascular health care.

NewYork-Presbyterian is thrilled to be joining forces with Cornell Tech and Cornell Bowers CIS to harness advanced technology and develop insights into the prediction and prevention of heart disease to benefit our patients, said Dr. Steven J. Corwin, president and chief executive officer of NewYork-Presbyterian. Together with our world-class physicians from Weill Cornell Medicine and Columbia, we can transform the way health care is delivered.

The collaboration aims to improve heart failure treatment, as well as predict and prevent heart failure. Researchers from Cornell Tech and Cornell Bowers CIS, along with physicians from Weill Cornell Medicine and Columbia University VP&S, will use AI and machine learning to examine data from NewYork-Presbyterian in an effort to detect patterns that will help physicians predict who will develop heart failure, inform care decisions and tailor treatments for their patients.

Artificial intelligence and technology are changing our society and the way we practice medicine, said Dr. Nir Uriel, director of advanced heart failure and cardiac transplantation at NewYork-Presbyterian, an adjunct professor of medicine in the Greenberg Division of Cardiology at Weill Cornell Medicine and a professor of medicine in the Division of Cardiology at Columbia University Vagelos College of Physicians and Surgeons. We look forward to building a bridge into the future of medicine, and using advanced technology to provide tools to enhance care for our heart failure patients.

The Cardiovascular AI Initiative will develop advanced machine-learning techniques to learn and discover interactions across a broad range of cardiac signals, with the goal of providing improved recognition accuracy of heart failure and extend the state of care beyond current, codified and clinical decision-making rules. It will also use AI techniques to analyze raw data from time series (EKG) and imaging data.

Major algorithmic advances are needed to derive precise and reliable clinical insights from complex medical data, said Deborah Estrin, the Robert V. Tishman 37 Professor of Computer Science, associate dean for impact at Cornell Tech and a professor of population health science at Weill Cornell Medicine. We are excited about the opportunity to partner with leading cardiologists to advance the state-of-the-art in caring for heart failure and other challenging cardiovascular conditions.

Researchers and clinicians anticipate the data will help answer questions around heart failure prediction, diagnosis, prognosis, risk and treatment, and guide physicians as they make decisions related to heart transplants and left ventricular assist devices (pumps for patients who have reached end-stage heart failure).

Future research will tackle the important task of heart failure and disease prediction, to facilitate earlier intervention for those most likely to experience heart failure, and preempt progression and damaging events. Ultimately this would also include informing the specific therapeutic decisions most likely to work for individuals.

At the initiative launch, Bala spoke of CornellsRadical Collaboration initiative in AI, and the key areas in which she sees AI a discipline in which Cornell ranks near the top of U.S. universities playing a major role in the future.

We identified health and medicine as one of Cornells key impact areas in AI, she said, so the timing of this collaboration could not have been more perfect. We are excited for this partnership as we consider high-risk, high-reward, long-term impact in this space.

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Collaboration will advance cardiac health through AI - EurekAlert