Artificial intelligence is making the appraisal space, which has been marred by instances of bias, more transparent.

According to Kenon Chen, executive vice president of strategy and growth at Clear Capital, his company is using machine learning and other AI tools to attain fairer assessments of properties.

"[We're] trying to solve a national problem, but also make it accurate at a community level," he said. "These types of tools help us build a national standard for how this should be approached and in the underwriting process, ensure that we're measuring the quality of the appraisal and the quality of the condition ratings against a repeatable standard."

Apart from implementing machine learning and computer vision to analyze and compare property values, the real estate valuation technology company is also pondering how generative AI can be integrated into the appraisal and underwriting process.

"There's a lot of great new possibilities that are being explored with generative AI and we're certainly looking at that as well," Chen said.

"There's still obviously very real challenges that we as an industry need to tackle, particularly in the racial housing gap," he added. "We still have a long way to go in closing that gap for especially black and brown homeowners and it's important to ensure that the techniques that we're using for underwriting for other types of decisioning like appraisal are consistent and accurate in every community."

National Mortgage News sat down with Chen to talk about how artificial intelligence is helping to create fairer standards of valuing homes and also how technology is set to change the appraisal space going forward. This interview has been edited and condensed.

Continued here:

Appraisals get transparency boost from AI, according to exec - National Mortgage News

This article has been reviewed according to ScienceX's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility:

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A cutting-edge machine learning model has been developed to predict soil organic carbon (SOC) levels, a critical factor for soil health and crop productivity. The innovative approach utilizes hyperspectral data to identify key spectral bands, offering a more precise and efficient method for assessing soil quality and supporting sustainable agricultural practices.

Soil health profoundly impacts agricultural productivity and ecological stability. Accurately assessing SOC levels is vital for enhancing crop yield and environmental sustainability. Traditional methods often fall short in precision and detail.

The new Perimeter-Area Soil Carbon Index (PASCI) addresses these gaps by utilizing hyperspectral imaging and machine learning algorithms to capture comprehensive soil characteristics. This approach not only refines SOC estimation but also supports targeted agricultural strategies and environmental monitoring, showcasing significant advancements over conventional methods.

In Geo-spatial Information Science on May 19, 2023, the researchers present their research from Central State University. The innovative tool, PASCI, employs machine learning to analyze hyperspectral data, significantly enhancing the measurement of soil carbon. PASCI provides a novel resource for scientists and agriculturists to more effectively map and assess soil health.

PASCI distinguishes itself by simultaneously analyzing multiple spectral bands to predict soil organic carbon, a method not available in current indices. This index uses a unique mathematical model to calculate the ratio of the perimeter to the area under spectral curves, pinpointing essential spectral bands that indicate SOC levels.

This approach reveals finer details about soil composition and variations across different landscapes, significantly enhancing the accuracy of SOC predictions. The robustness of PASCI was validated through extensive regression analysis, demonstrating a strong correlation with actual SOC measurements (r2 = 0.76). The index's comprehensive scope allows for better adaptation in diverse agricultural settings, potentially leading to more precise farming practices and improved crop yields.

The lead researcher says, "Our findings represent a leap forward in the remote sensing of soil organic carbon. PASCI's ability to integrate various spectral regions provides a more nuanced and accurate measure of SOC, which is vital for advancing precision agriculture and promoting sustainable land use."

PASCI's applicability is vast, offering the potential to integrate with both hyperspectral and multispectral imaging technologies. This advancement could enable large-scale detailed mapping of soil organic carbon, beneficial for agricultural planning and environmental monitoring.

The index's development aligns with the growing need for tools to assess and manage soil health, promising to enhance agricultural practices and contribute to global sustainability efforts.

More information: Eric Ariel L. Salas et al, Perimeter-Area Soil Carbon Index (PASCI): modeling and estimating soil organic carbon using relevant explicatory waveband variables in machine learning environment, Geo-spatial Information Science (2023). DOI: 10.1080/10095020.2023.2211612

Provided by Wuhan University

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Mapping soil health: New index enhances soil organic carbon prediction - Phys.org

WorldView, a leading provider of integrated healthcare technology to the top home health and hospice EHR/EMR platforms, announced the upcoming launch of Referral AI, an enhancement to automate intake referrals using a custom AI/ML model built specific for the healthcare industry.

Referral AI uses AI/ML (Artificial Intelligence/Machine Learning) to scan and analyze dense referral document packets in seconds, detecting false positives and negatives, using custom rules to send confirmed referrals to the EHR/EMR system.

AiThority.com Latest News: Alation Has Announced an Enhanced Integration With Snowflake Horizon

In a recent survey by WorldView, confidence in a referral being acted upon quickly was a top-ranking factor for 85 percent of referring partners. WorldViews Referral AI was designed to help agencies win more business and eliminate manual workflows related to the overload of documents in their inbox.

Home health and hospice agencies receive many forms of electronic documents in their inbox, including referrals for new patient service. Referrals must be acted on quickly, but with documents being dozens of pages, they often sit unread or, worse, are missed or overlooked. Over time, the referral can become invalid, resulting in lost revenue for the agency and posing a risk of delayed service for patients.

Referral AI is a custom AI/ML model built specifically for the home-based care industry and trained on 22+ years of data, outperforming off-the-shelf AI/ML models for similar tasks in speed and accuracy.

Referral AI benefits home health and hospice agencies through cutting-edge features:

Read:Impel adds WhatsApp messaging to AI-Powered Customer Lifecycle Management Platform

Why Referral AI matters:

When we started developing our Referral AI technology, we saw first-hand how other solutions released features that inevitably created more downstream issues, saidJared Robey, SVP at WorldView. We leveraged our extensive dataset to build and train our AI/ML model, ensuring that referrals are identified accurately and routed to an intake team for prioritization. This investment allows WorldView to continue pushing automation limits to enhance user experience and increase financial success.

WorldViews Referral AI prioritizes rapid patient care and reduces the burden on back-office staff. By drastically cutting down the time needed for the intake process, Referral AI enables care coordination to begin almost immediately. The solution provides an organized and insightful overview of the referral packet, ensuring clinicians have quick access to the patients clinical history, reasons for care, and critical findings. This clarity allows admitting clinicians to focus on delivering high-quality care without sifting through extensive documentation.

Read More: L2L Introduces Powerful AI Functionality to Empower Frontline Manufacturing Teams

[To share your insights with us as part of editorial or sponsored content, please write topsen@martechseries.com]

Excerpt from:

WorldView Launches Referral AI to Boost Home Health and Hospice Revenue - AiThority

By Michael Leedom

The modest stethoscope has joined the Artificial Intelligence (AI) revolution, tapping into the power of machine learning to help health-care providers screen for diseases of the heart and lung.

This year, NCH Healthcare in Naples, Fla., became the first health-care system in the U.S. to incorporate AI into its primary care clinics to screen for heart disease. The health technology company Eko Health supplied primary care physicians with digital stethoscopes linked to a deep-learning algorithm. Following a 90-day pilot program involving more than 1,000 patients with no known heart problems, the physicians discovered 136 had murmurs suggestive of structural heart disease.

Leveraging this technology to uncover heart valve disease that might otherwise have gone undetected is exciting, says Bryan Murphey, President of the NCH Medical Group, which signed an annual agreement in January with Eko to use stethoscopes with the AI platform. The numbers made sense to help our patients in a non-invasive way in the primary care setting, says Murphey.

Ekos AI tool the SENSORA Cardiac Disease Detection Platform enables stethoscopes to identify atrial fibrillation and heart murmurs. The platform added another algorithm,clearedby the U.S. Food and Drug Administration (FDA) in April, for the detection of heart failure using the Eko stethoscopes built-in electrocardiogram (ECG) feature.

AI-enhanced stethoscopes showed more than a twofold improvement over humans in identifying audible valvular heart disease, according to astudypublished inCirculationin November 2023. The AI showed a 94.1 per cent sensitivity for the detection of valve disease, outperforming the primary care physicians 41.2 per cent. The findings were confirmed with an echocardiogram of each patient.

Stethoscopes join the growing number of AI health-care applications that promise increased efficiency and improved diagnostic performance with machine learning. In recent years, the FDA has cleared hundreds of AI algorithms for use in medical practice. But as the health-care field employs AI for more services, skeptics point to risks posed by over-reliance on this black box, including the potential biases built into AI datasets and the gradual loss of clinician skills.

Since its adoption more than 200 years ago, the stethoscope has served as both a routine exam tool and a visible reminder of the doctors training. It is recognizable worldwide and, for most clinicians, has remained an analog instrument. The first electronic stethoscopes were created more than 20 years ago and feature enhancements to amplify sound and allow for digital recording.

Analog and digital stethoscopes both rely on the ability of the health-care provider to hear and interpret the sounds, which may be the first indication a patient may have a new disease. However, this is not a skill every health-care practitioner masters. The faint, low-pitched whooshing of an incompetent heart valve or the subtle crackling of interstitial lung disease may go unnoticed even by the ears of experienced physicians.

Enter AI, which can mimic the human brain using neural networks consisting of algorithms that, in the case of stethoscopes, are trained with thousands of heart or lung recordings. Instead of relying on explicit program instructions, an AI system uses machine learning to train itself through advanced pattern recognition.

The effectiveness of artificial neural networks to diagnose cardiovascular disease has been demonstrated in controlled clinical trials.

AI improved the diagnosis of heart failure by analyzing ECGs performed on more than 20,000 adult patients in a randomized controlled trial published inNature Medicine. The intervention group was more likely to be sent for a confirmatory echocardiogram, resulting in 148 new diagnoses of left ventricular systolic dysfunction.

A neural network algorithm correctly predicted 355 more patients who developed cardiovascular disease compared to traditional clinical prediction based on American College of Cardiology guidelines, according to a cohortstudyof nearly 25,000 incidents of cardiovascular disease.

These machines are very good at finding patterns that are even beyond human perception. But theres both the power as well as the risk, says Paul Yi, Director of the University of Maryland Medical Intelligent Imaging Center.

The risks include limitations in performance if AI models are not properly trained. The accuracy of the AI algorithm depends on the collection of sufficient data that is representative of the population at large.

These AI models require a large amount of data, and these data are not easy to come by.

The generalizability is a big issue, says Gaurav Choudhary, Director of Cardiovascular Research at Brown University. These AI models require a large amount of data, and these data are not easy to come by. Choudhary notes that once an algorithm is approved by the FDA, it cannot be simply revised as new recordings become available. Changes to a particular AI algorithm require a new submission to the FDA before use.

In January 2024, the World Health Organization published newguidelinesfor health-care policies and practices for AI applications. Its authors warned of several risks inherent in the use of AI tools, including the existence of bias in datasets, the transparency of the algorithms employed and the erosion of medical provider skills.

AI algorithms that interpret heart and lung recordings may not have been trained on the full spectrum of possible sounds if the data does not include a wide range of patients and ambient noises.

This technology has to be validated across a variety of murmurs in a variety of clinical environments and situations, says Andrew Choi, Professor of Medicine and Radiology at George Washington University. Many of our patients are not the ideal patients, he adds, noting that initial validation typically involves patients with clear heart sounds. In real world practice, there will be older patients, obese patients and noisy emergency departments that may compromise the precision of the AI model.

Another complication is the inscrutable nature of the algorithm. Without a clear understanding of how these systems make decisions, it may be difficult for health-care providers to discuss a management plan with patients, particularly if the AI output appears incompatible with other clinical information during the examination.

Explainability is sort of a holy grail, says Paul Friedman, Chair of the Department of Cardiovascular Medicine at Mayo Clinic and one of the developers of the AI tech that Eko Health uses. Over time, he says, more studies may elucidate how these systems process information. AI uncertainty is similar to our incomplete understanding of how certain medications actually work, he suggests. Both are used because they are consistently effective.

Im not dismissive of the importance of trying to crack the black box, but I think thats a subject for research, he says.

The introduction of AI in the exam room could both enhance diagnostic performance while disrupting the relationship between health-care provider and patient. The provider may become complacent and gradually dependent on AI for answers to clinical questions, while the patient may feel that the care is becoming depersonalized and lose confidence in the doctor.

The subconscious transfer of decision-making to an automated system is called automation bias, one of many cognitive biases the health-care provider must confront. There are many reasons providers may forgo medical training and uncritically accept the heuristics of AI, including inexperience, complex workloads and time constraints, according to a systematicreviewof the phenomenon.

It is still unclear how AI will ultimately influence the physician-patient interaction, says Yi. I think thats kind of the last mile of AI in medicine. Its this human-computer interaction piece where we know that this AI works well in the lab, but how does it work when it interacts with humans? Does it make them second guess what theyre doing? Or does it give them false confidence?

The number of AI-enhanced devices submitted to the FDA hassoaredsince 2015, with almost 700 AI medical algorithmsclearedfor market. Most applications are for radiology. AI is already being integrated into academic medical centres across North America for a variety of tasks, including diagnosing disease, projecting length of hospitalization, monitoring wearable devices and performing robotic surgery.

At Unity Health in Toronto, more than 50 AI-based innovations have been developed to improve patient care since 2017. One of these is a tool used at St. Michaels Hospital since 2020 called CHARTWatch, which sifts electronic health records, including recent test results and vital signs, to predict which patients are at risk of clinical deterioration. The algorithm proved to be lifesaving during the COVID pandemic, leading to a 26 per cent drop in unanticipated mortality.

I think AI is really going to transform health care, says Omer Awan, Professor of Radiology at the University of Maryland School of Medicine. He is not concerned that AI will take over physician jobs, instead predicting that AI will continue to improve efficiency and help reduce physician burnout.

Research continues on how best to incorporate AI into the primary care setting, including ethical issues such as data privacy, legal liability and informed consent. The adoption of AI may infringe on patient autonomy if medical decisions are made using algorithms without regard for patient preferences, according to a literaturereview.

Murphey says he is eager to see Eko Healths AI-paired stethoscopes improve the screening for early heart disease but remains cautious about too much use of technology.

I want to stay connected to the patient. I take pride in my patient examinations, he says. I think thats one of the important things we provide to patients in the primary care setting, and Im not looking to sever that part of the relationship.

This post was previously published on HEALTHYDEBATE.CA and is republished under a Creative Commons license.

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Machine learning and AI enable early lameness detection - Farmers Guardian

Meta will start training its AI models using everyone's social media posts though European Union users can opt out, a luxury the rest of the world won't enjoy.

The move, which the Facebook parent detailed in an announcement today, is ostensibly to bring its machine-learning systems to Europe.

Meta has so far not included its European userbase in its AI training data, presumably to avoid legal conflict with the continent's privacy regulations. Now it's pushing ahead with that despite complaints.

"To properly serve our European communities, the models that power AI at Meta need to be trained on relevant information that reflects the diverse languages, geography and cultural references of the people in Europe who will use them," the social media titan said.

"To do this, we want to train our large language models that power AI features using the content that people in the EU have chosen to share publicly on Meta's products and services."

As training AI from user data is doubtlessly going to be contentious in Europe, Meta has attempted to cover itself in two ways. Firstly, when it says "public content," Meta means posts, comments, photos, and other content posted on its social media platforms by users over the age of 18. Private messages are, apparently, strictly verboten from the training data.

Meta also says it has sent billions of notifications to European users since May 22 to give them a chance to decline before the AI training rules kick in worldwide on June 26. The Instagram goliath says any user can decline, no questions asked, and that their posts won't be used to train AI models now or ever.

This is substantially different from the rest of the world, where opting out just isn't a choice. Granted, it's already too late to opt out for training data used for Meta's LLaMa 3, but even training for future models is mandatory for Facebook and Instagram users outside of the EU. Perhaps users outside of Europe will be able to choose to opt out in the future, but for now it's a feature exclusive to the EU.

Although Meta likely feels that it's in a good position to start using European user data, it's hard to imagine there being no pushback at all. Before the social media giant even made its public announcement, it signaled its intentions via an update to its privacy policy last week. That prompted consumer privacy advocacy group noyb to file complaints across Europe.

Noyb claims the collection of user data needs to be opt-in, not opt-out, by default. The fact that data can't really be scrubbed from an LLM or other AI model is also likely to cause problems due to the European Union's Right to be Forgotten.

Plus, Meta and the EU are not on the best of terms. Just this year the EU launched probes into Meta including one concerning child safety and another about misinformation surrounding the now-concluded EU parliamentary elections. While it's not clear if Meta will have its way in the end, it's hard to imagine there not being a challenge against the social network at some point or another.

See the article here:

Meta will use your social media posts to train its AI. Europe gets an opt out - The Register

Pity OpenAIs HR department. Since the start of the year the maker of ChatGPT, the hit artificial-intelligence (AI) chatbot, has lost about a dozen top researchers. The biggest name was Ilya Sutskever, a co-founder responsible for many of the startups big breakthroughs, who announced his resignation on May 14th. He did not give a reason, though many suspect that it is linked to his attempt to oust Sam Altman (pictured), the firms boss, last December. Whatever the motivation, the exodus is not unusual at OpenAI. According to one estimate, of the 100-odd AI experts the firm has hired since 2016, about half have left.

That reflects not Mr Altmans leadership but a broader trend in the technology industry, one that OpenAI itself precipitated. Since the launch of ChatGPT in November 2022, the market for AI labour has been transformed. Zeki Research, a market-intelligence firm, reckons that around 20,000 companies in the West are hiring AI experts. Rapid advances in machine learning and the potential for a platform shifttech-speak for the creation of an all-new layer of technologyhas changed the types of skills employers are demanding and the places where those who possess them are going. The result is a market where AI talent, previously hoarded at tech giants, is becoming more distributed.

Original post:

The war for AI talent is heating up - The Economist

Representational Image. Pic Courtesy/iStock

From reshaping technology to altering industries, Artificial Intelligence (AI) is set to impact the world more profoundly than any previous innovation. Not just in tech but automation in finance, healthcare, retail, gaming and almost every other domain is driving ahead AI's growing intervention in our surroundings.

Yet, concerns about automation have long overshadowed the future of work. While AI will enhance certain jobs, it is arguable that this may lead to job displacement for others. According to a recent report by Goldman Sachs, around 300 million full-time jobs globally could be vulnerable to automation by AI This includes various sectors where repetitive and data-driven tasks are more common.

But proponents of AI believe otherwise. Professor Fei-Fei Li, co-director of the Stanford Institute for Human-Centered Artificial Intelligence remarks that "Artificial intelligence is not a substitute for human intelligence; it is a tool to amplify human creativity and ingenuity."

As AI continues to open new doors, upGrad's Mayank Kumar points out the future potential: According to the World Economic Forum (WEF), AI will generate 12 million new jobs by 2025 - an opportunity we cannot miss." This shift necessitates a change in how young professionals are trained, particularly in India, where a large number of youth are at the cusp of joining the workforce.

To identify the most sought-after AI skills in the current Indian job market, Midday.com roped in experts from various sectors including health, education, gaming, filmmaking and finance. Here are the key takeaways from the interactions:

Top AI skills in the healthcare ecosystem In the contemporary landscape, where data availability is abundant, Harshit Jain, MD, Founder & Global CEO, Doceree shares that the skill of data extraction and analysis emerges as a paramount AI proficiency gaining popularity within healthcare. Through smart analysis of healthcare provider (HCP) data, AI can identify patterns in HCP behaviour, and enable pharmaceutical manufacturers to empower HCPs with knowledge or information relevant to their clinical workflow, thereby optimising patient outcomes.

Additionally, AI when used to study patient-level data in a compliant manner, offers invaluable insights into individual health histories/trajectories. This can help with personalised interventions and preventative measures.

When asked about developing a knack for AI, Jain remarked that equipping with AI skills requires practice. In his opinion, hands-on experience with real-world datasets and industry-standard tools is invaluable for translating theory into practicality However, for beginners to understand the nitty-gritty of technology, it is ideal to go for courses that are a comprehensive blend of theoretical knowledge and practical applications.

"A few online courses that I feel can offer such a blend include, AI in Healthcare Specialization Course by Stanford University; Artificial Intelligence in Pharma and Biotech by MIT or AI for Healthcare by University of Manchester. Key components of such courses include understanding AI fundamentals, machine learning algorithms and deep learning techniques relevant to healthcare data analysis. Having said that, an emphasis on ethical considerations, privacy concerns and regulatory compliance is crucial due to the sensitive nature of medical data," he added.

(L-R) Harshit Jain, MD, Founder & Global CEO, Doceree and Mayank Kumar, Co-founder & MD, upGrad

Top AI skills in business administration Recently, upGrad has introduced a Doctorate of Business Administration in Digital Leadership with Golden Gate University, San Francisco. In less than a month, over 1,500 individuals with over 10-12 years of work experience have enrolled in the program, while overall enrollments in the GenAI program crossed 100 in just April 2024.

Other top AI skills in business involve data analysis, proficiency in machine learning to automate processes and understanding natural language processing (NLP) to enhance customer interactions and streamline communication. An industry-first pedagogy to strengthen AI leadership in India, is in the making, informs Mayank Kumar, co-founder & MD of upGrad.

Today, ed-tech companies offer AI courses for professionals looking to upskill at all stages - from freshers to seasoned experts. While free courses, certifications and boot camps are a quick way to skill up, many long-format courses in partnership with top universities have also enabled professionals to build on their expertise and become thought leaders in GenAI.

"There's always chatter about new technologies disrupting education, but instead of just disrupting, we should focus on leveraging these technologies to enhance our skilling and lifelong learning," outlines the Mumbai-based ed-tech expert.

Top AI skills in technology Harnessing AI's potential, there is a surge in demand for professionals skilled in machine learning and AI ethics, informs Siddharth Shahani, executive president, Atlas Skilltech University, Mumbai. "It's not just about coding algorithms; it's about understanding AI's profound impact on society."

For those entering this field, he advises starting with the fundamentals. A growing number of universities are offering tech-enabled education like developing chatbots, image classifiers or predictive models and participating in hackathons. This blend of theory and practice equips graduates to drive innovation in any sector.

"Many of the university's students have gone on to roles at leading tech companies like Google, IBM and TCS, demonstrating the value of a comprehensive, industry-aligned education in today's AI-driven world," adds Shahani.

(L-R), Siddharth Shahani, Executive President, Atlas Skilltech University and Venkat Malik, co-founder and CEO, Tidal7

Top AI skills in gaming As gaming becomes more immersive, intelligent, intuitive and responsive mimicking humans, the need for AI skills and capabilities is bound to grow substantially, outlines Venkat Malik, co-founder and CEO of a Mumbai-based digital marketing agency - Tidal7.

He informs that "Machine Learning, Deep Learning, NLP, Simulation & Modelling are all specific skills that are likely to be useful in the gaming industry as games become more realistic, human-like, agile and responsive." When asked about specific courses to acquire these skills, he directed us to Coursera and Udemy. "There are beginner's guides and specialised courses which offer introductory and advanced courses which can help in game design and development."

In various aspects of game development, such as character behaviour, procedural generation and adaptive difficulty systems, AI programming and decision-making algorithms are emerging as useful skills when building these models. Courses also equip professionals with skills used in the creation of game worlds, terrains, textures, etc, customising the level of difficulty to the player's skill level and defining their characters.

The game development platforms or engines that are worth experimenting with include Unity, and Unreal Engine for building AI-led games, adds Malik.

Also Read: AI simplifies decision-making in real estate operations, here's how

Top industrial AI skills Adding on to the integration of AI in industrial growth, Deepak Verma, the COO of Images Bazaar informs that manufacturing industries are increasingly employing AI-powered tools and systems to save costs and enhance patient outcomes. Robotics and computer vision amongst other skills mentioned above are the most in-demand AI capabilities at the moment, he adds.

An excellent option for novices to learn these in-demand AI skills is to start with online classes and resources. Before moving on to more complex areas, he advises starting with introductory courses that give a strong foundation in AI ideas. Additionally, when you start your AI adventure, looking through beginner-friendly AI networks and forums or attending meetups relevant to AI can offer helpful networking possibilities and support.

Additionally, participating in real-world projects and practical experiences is essential to developing and demonstrating competency in AI abilities including deep learning and data science. Sentiment analysis, image identification and recommendation systems are a few useful project ideas. Working on these projects will provide you with invaluable practical experience using AI techniques to solve real-world issues, explains Verma.

When asked about the relevance of degrees in AI, he says "In the AI job market, degrees, self-taught abilities and certifications become crucial. Achieving a balance between these techniques entails employing online courses, workshops and real-world projects; along with self-taught skills. The goal is to develop a broad skill set that can satisfy the needs of the artificial intelligence sector."

(L-R), Deepak Verma, the COO of Images Bazaar and Anubhav Srivastava, head of AI at Stupa Sports Analytics

Top AI skills in sports management Reportedly, AI has enabled sports companies to enhance performance analytics and player training. "The focus is on transforming raw data into actionable intelligence, enabling informed decision-making for players, coaches and sports organisations," shares Anubhav Srivastava, head of AI at Stupa Sports Analytics.

The integration of AI in sports demands a specific skill set, particularly for roles centred on data analytics, predictive modelling and real-time performance tracking. "The most sought-after AI skills in the sports industry include data analysis and statistics, machine learning and deep learning, computer vision, programming, and real-time data processing," informs Srivastava.

For those aspiring to entre the field of AI in sports, a combination of educational background, certifications and practical experiences is essential. A bachelor's degree in computer science, data science, artificial intelligence or a related field provides a strong foundation.

Top AI skills in broadcast and filmmaking When it comes to the moving image, Gen AI is transforming the filmmaking and broadcast industries in a variety of ways. It brought with it efficiency in colour correction, animation, environmental effects and more labour-intensive tasks which took days to render, informs Abir Aich, executive vice president, Arena Animation, MAAC & The Virtual Production Academy by Aptech.

These technologies not only boost creativity, increase efficiency and streamline content creation production processes, but they are also being used to automate and improve various aspects of production, such as scriptwriting, creative visualisation, storyboarding and concept design, thereby driving innovation and efficiency in these creative fields, he adds.

For example, Disney's "The Lion King" (2019) used advanced AI techniques for creating photorealistic animal characters and environments, blending live action with CGI seamlessly. Then you have Warner Bros.' "Gemini Man" (2019) where AI was utilised to de-age actor Will Smith, creating a younger version of his character through deep learning and CGI. The other example is how by leveraging AI, Netflix can predict what shows or movies a user might enjoy based on their viewing history, enhancing the personalised viewing experience.

When asked about AI-integrated courses for film producers, Aich shares that it is critical to specialise in Gen AI applications for media, such as Midjourney, Runway ML, Stable Diffusion, Adobe Firefly and so on, and apply them at various stages of the creative visualisation process. Practical experience gained through industry events/projects, internships and other opportunities is critical.

Recommended courses include Andrew Ng's "Deep Learning Specialisation" and IBM's "Introduction to Artificial Intelligence (AI)" on edX. Specialised programs include CG Spectrum's "Real-Time 3D and Virtual Production", MAAC's specialised programs like Gen AI-powered AD3D Edge Plus and ADVFX Plus, Virtual Production Specialist from The Virtual Production Academy and community resources like Kaggle and GitHub also offer valuable learning opportunities.

AI tools for aspiring filmmakers:

Lastly, he notes that AI experts with filmmakers and broadcasters make for a very interesting collaboration. To integrate AI successfully, filmmakers should identify the scope of usage, invest in training, leverage existing tools, start with pilot projects and collaborate with AI enablers.

(L-R) Abir Aich, Executive Vice President, Arena Animation, MAAC and Sarvagya Mishra, Co-founder & Director of Superbot

Why agencies are priortising employees adept in AISarvagya Mishra, co-founder & director of the AI-powered voice agent startup - Superbot, shares that sectors spanning manufacturing and logistics to finance and communications are leveraging AI to catalyse efficiency, drive innovation and propel growth.

The capacity to cleanse, explore and extract insights from vast datasets is paramount. A robust mathematical foundation, fluency in programming languages such as Python, and hands-on experience in constructing, testing and optimising AI models are primary requisites. Fortunately, numerous pathways exist to cultivate these skills as elucidated by Mishra.

Academic institutions are rapidly expanding AI and data science degree programs to meet the soaring demand. One can pursue master's curricula or specialised short-term courses and boot camps. Moreover, immersing oneself in projects and AI coding challenges is invaluable for practical application.

The exigency is palpable - a striking 60 per cent of public IT professionals identify the AI skills gap as their paramount impediment, according to one study. With AI poised to yield hundreds of millions of hours in savings and billions in cost reductions for the public sector annually, agencies will prioritise hiring and training employees adept in these cutting-edge capabilities.

Irrespective of the chosen path, continuous upskilling and practical application will be critical to maintaining industrial AI skills commensurate with this rapidly evolving domain. Those who master these in-demand skills will be optimally positioned for long-term career growth.

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MIDDAY EXPLAINS: Surge in demand for AI ethics, machine learning and data analysis, must-have AI skills revealed - mid-day.com

By Chad Cox

Production Editor

Embedded Computing Design

June 10, 2024

News

London, United Kingdom / Toronto, Canada. In 2023, Alphawave Semi joinedArm Total Design and leveraged its collaboration with Arm to design an innovative compute chiplet built on Arm's Neoverse Compute Subsystems (CSS). The platform is ideal for artificial intelligence/machine learning (AI/ML), high-performance compute (HPC), data centers, and 5G/6G networking infrastructure applications.

According to the company, its chiplet-based custom silicon design solution adds a differentiator in its portfolio including IO extension chiplets, memory chiplets, compute chiplets, and Alphawave Semis ultra-high-speed connectivity IP and advanced packaging proficiencies.

Our Arm-based compute chiplet is a critical component in Alphawave Semis custom silicon platform and a demonstration of both our IP, SoC and packaging capabilities and our successful strategic partnership with Arm, said Mohit Gupta, Senior VP & GM, Custom Silicon & IP, Alphawave Semi.

Alphawave Semis portfolio features an Arm Neoverse N3 CPU core cluster and the Arm Coherent Mesh Network (CMN) ensuring efficient, scalable performance. Accessible on industry-leading process nodes, the SoCs allow quick deployment of high-performance digital infrastructure in order to create custom silicon solutions.

Eddie Ramirez, Vice President of Go-to-Market, Infrastructure Line of Business, Arm offered, Alphawave Semis new advanced compute chiplet is a fantastic example of how industry-leading companies are leveraging the performance-optimization and power efficiency benefits of Neoverse CSS to get to market faster and power the next-generation AI and HPC workloads.

For more information, visitawavesemi.com.

Chad Cox. Production Editor, Embedded Computing Design, has responsibilities that include handling the news cycle, newsletters, social media, and advertising. Chad graduated from the University of Cincinnati with a B.A. in Cultural and Analytical Literature.

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Alphawave Semi Leverages Arm's Neoverse Compute Subsystems for AI/ML Applications - Embedded Computing Design

Is AI a boon or a bane to job security? A security tool or a vulnerability? Mature enterprise technology or immature toy? Essential enterprise technology or threat to humanity?

According to survey respondents from InformationWeek's latest State of AI Report, its all of the above.

More than a year after generative artificial intelligence became widely available to the public, we polled 292 people directly involved with AI at their organizations.

Unsurprisingly, results reveal that adoption of AI is widespread, and businesses are using the technology for a wide range of different taskswith 85% of respondents describe their organizations approach to AI as pioneering or curious but cautious.

But expectations about this novel technology are also quite different from reality. So far, AI hasnt significantly affected headcount, and respondents overwhelmingly feel their own jobs are safe from its reach.

On the other hand, concerns around data security, hallucinations, and the reliability of outcomes are weighing on respondents' minds. 53% say that, if unchecked, artificial intelligence poses a threat to humanity.

Download this free report to learn how IT departments are investing in AI now and whats guiding their plans for the future.

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IT Pros Love, Fear, and Revere AI: The 2024 State of AI Report - InformationWeek