LLM in a Flash

Published on December 12, the new study titled “LLM in a Flash: Efficient Large Language Model Inference with Limited Memory” can revolutionize the iPhone experience. It could bring a more immersive visual experience and make complex AI systems accessible on iOS devices. A recent research paper by Apple reveals a groundbreaking method that can assist in implementing AI on iPhones.

New: 10 AI ML In Personal Healthcare Trends To Look Out For In 2024

3D animated avatars from single-camera footage

Apple researchers present HUGS (Human Gaussian Splats) as a method to create 3D animated avatars from single-camera footage in the first study. In a statement made by principal author Muhammed Kocabas, the researchers claimed that their system could automatically separate a static scene from an animated human avatar in as little as 30 minutes using only a monocular video with a modest number of frames (50-100).

Read: Top 10 Benefits Of AI In The Real Estate Industry

Future demands of AI-infused services

Apple is looking ahead to the future demands of AI-infused services as it considers incorporating these breakthroughs into its product selection, which might improve its gadgets even further. If Apple’s new memory-allocation feature works as advertised, it might pave the way for a whole new category of apps and services to take advantage of LLMs in ways that weren’t possible before.

In addition, Apple is contributing to the larger AI community by publicizing its research, which could encourage other improvements in the field. That Apple is willing to do this shows how seriously it takes its role as a technological leader and its dedication to expanding human potential.

Read:The Top AiThority Articles Of 2023

Flash storage optimization

Using flash storage optimization, this method streamlines large LLMs. Another major development will occur when Apple incorporates sophisticated AI inside the iPhone. Two new research papers showcased this month by the Cupertino-based tech behemoth declared substantial advancements in AI. The study uncovered novel methods for efficient inference of language models and 3D avatars. This research delves into the difficulty of keeping model parameters in flash memory, running them into DRAM on demand, and executing LLMs that use more DRAM than is available. Data transfers from flash memory can be optimized with the use of the Inference Cost Model, which takes flash and DRAM characteristics into account.

To back up their claim, the researchers have utilized models like Falcon 7B and OPT 6.7B. According to the research, compared to conventional approaches, the models increased CPU speed by 4-5 times and GPU speed by 20-25 times.

Read: State Of AI In 2024 In The Top 5 Industries

Why the users should be happy?

Users of Apple products, such as the iPhone, may profit substantially from the results of the study on efficient LLM inference with limited memory. Users will get access to greater AI capabilities with strong LLMs running efficiently on devices with limited DRAM, like as iPhones and iPads. Better language processing, smarter voice assistants, better privacy, maybe less internet bandwidth utilization, and, most significantly, making advanced AI available and responsive to every iPhone user—these are all features that come with the iPhone.

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How is AI helping Researchers Identify a New Class of Antibiotic?

To combat diseases caused by bacteria that are resistant to many antibiotics, artificial intelligence has been essential in the discovery of a new class of medications. This may be useful in the fight against antibiotic resistance, which is a growing problem that killed over 1.2 million people in 2019 and will likely continue to do so for decades to come. A novel antibiotic that can kill a type of bacterium responsible for many drug-resistant diseases has been identified by researchers at MIT and McMaster University using an artificial intelligence algorithm.

Read:The Top AiThority Articles Of 2023

The medicine has the potential to battle Acinetobacter baumannii, a type of bacteria commonly found in healthcare facilities and a cause of pneumonia, meningitis, and other severe diseases if it were to be developed for use in patients. Wounds sustained by troops serving in Iraq and Afghanistan are also frequently infected with this particular bacterium. Using a machine-learning model they trained to determine if a chemical compound inhibits the growth of A. baumannii, the researchers were able to identify the novel medicine from a library of roughly seven thousand potential medicinal molecules.

Read: State Of AI In 2024 In The Top 5 Industries

What are its features?

While very few new antibiotics have been created during the past several decades, many pathogenic bacteria have grown progressively resistant to current ones.

Collins, Stokes, and Regina Barzilay, a professor at MIT and co-author of the current paper, set out a few years ago to tackle this increasing problem using machine learning, an AI technique that can learn to identify patterns in massive datasets. Collaborating with MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health, Collins and Barzilay intended to find novel medicines with structurally distinct molecular bonds using this method.

New: 10 AI ML In Personal Healthcare Trends To Look Out For In 2024

First, they showed that they could train a machine-learning system to find chemical compounds that could stop E. coli from growing. The researchers named the molecule halicin after the fictitious AI system from “2001: A Space Odyssey.” The algorithm produced it from a screen of over 100 million molecules. In addition to killing E. coli, they demonstrated that this chemical might eradicate other treatment-resistant bacterial species. After training the algorithm, scientists fed it data from the Broad Institute’s Drug Repurposing Hub, which included 6,680 novel molecules. A few hundred high-quality results were produced by this analysis, which did not take more than two hours. Researchers focused on compounds with structures different from current antibiotics or molecules from the training data, choosing 240 to test experimentally in the lab.

Read: Top 10 Benefits Of AI In The Real Estate Industry

A New Class of Antibiotic Candidates That Can Kill a Drug-Resistant Bacterium

Nine antibiotics, including a highly effective one, were produced during those tests. This chemical, which was first investigated for its use as a diabetes medication, was found to be highly efficient against A. baumannii but inactive against other bacterial species such as Pseudomonas aeruginosa, Staphylococcus aureus, and carbapenem-resistant Enterobacteriaceae.

Antibiotics are highly prized for their “narrow spectrum” killing capabilities, which reduces the likelihood of bacteria quickly developing resistance to the medicine. A further perk is that the medicine will probably not harm the good bacteria already present in the human digestive tract, which helps to prevent opportunistic illnesses like Clostridium difficile.David Braley Center for Antibiotic Discovery, Weston Family Foundation, Audacious Project, C3.ai Digital Transformation Institute, Abdul Latif Jameel Clinic for Machine Learning in Health, DARPA Accelerated Molecular Discovery, Canadian Institutes of Health Research, Genome Canada, McMaster University’s Faculty of Health Sciences, Boris Family, a Marshall Scholarship, and the Department of Energy Biological and Environmental Research program were among the organizations that contributed to the funding of this research.

The post Using AI, Researchers Identify a New Class of Antibiotic Candidates That Can Kill a Drug-Resistant Bacterium appeared first on AiThority.

Transforming Healthcare With AI

According to predictions, the worldwide market for artificial intelligence (AI) in healthcare would expand from an initial valuation of $15.1B in 2022 to more than $187.95B in 2030, a compound annual growth rate (CAGR) of 37 percent. The North American artificial intelligence healthcare industry was worth USD 6.8 billion in 2022.

By 2024, artificial intelligence is expected to make great strides in the medical field. Deep learning algorithms improve the accuracy of X-ray, MRI, and CT scan interpretation, which is useful in medical imaging and diagnostics. AI plays a crucial role in the drug discovery process by sifting through large datasets in search of promising compounds and automating development. Using a patient’s unique genetic and molecular profile, personalized medicine employs AI to create individualized treatment plans. Using natural language processing to glean insights from unstructured clinical data, predictive analytics helps with patient outcome prediction and at-risk population management.

The research examines six key areas where AI directly affects the patient and three sectors of the healthcare value chain that might gain from more scaling of AI. It also looks at specific instances of current AI solutions in healthcare.

With the help of AI, telemedicine and RPM may provide patients with up-to-the-minute health information. To guarantee ethical AI use, legislative frameworks, and ethical concerns have come to the fore. Instead of seeing AI as a replacement for human healthcare providers, the focus is on how AI systems may work in tandem with them. The picture highlights the revolutionary integration of AI into several aspects of healthcare, which holds great promise for better diagnoses, personalized treatments, and overall patient care.

Read Top 20 Uses of Artificial Intelligence In Cloud Computing For 2024

10 Personal Healthcare Trends To Look Out For In 2024

AI and ML are transforming personal healthcare by enhancing diagnostics, treatment, and overall wellness.

Here are 10 trends to watch out for in AI and ML in personal healthcare in 2024:

1. Personalized Treatment Plans: AI will analyze individual health data to create personalized treatment plans, including medication regimens and lifestyle recommendations. There will be an uptick in the number of chances for patients to get individualized healthcare in 2023. Precision medicine is a part of this, and it involves making individualized medicines and treatment plans for groups of patients based on characteristics like age, genetics, and risk factors, rather than using a cookie-cutter approach. By considering an individual’s genetic information, or genome, the most cutting-edge customized healthcare systems can assist doctors in determining the efficacy of medications and the likelihood of adverse effects. These forecasts are occasionally assisted by AI and ML systems.
2. AI for Mental Health: AI-driven mental health apps and platforms will provide therapy, counseling, and support for individuals dealing with mental health issues. Forecasts indicate that by 2023, the market for AI tools, particularly ML tools used in healthcare, will surpass $20 million. There is a lot of evidence that AI-aligned technologies like pattern recognition algorithms, computer vision, and natural language processing may improve healthcare. These technologies are now widely used in the industry and will only become more so as 2023 progresses. Drug discovery is one area where AI is used; it helps predict the results of clinical trials and the side effects of new drugs. Another area is medical imaging analysis, where AI is used to use computer vision algorithms to detect early warning signs of disease in X-rays or MRI scans. It has also shown promise in the diagnosis and treatment of neurological diseases, such as Alzheimer’s and Parkinson’s.
3. Wellness and Lifestyle Management: AI will help individuals make healthier choices by analyzing data from wearables and providing personalized fitness and nutrition guidance. In 2023, wearable technology will be more popular among both patients and doctors for remote patient monitoring and health and fitness tracking. Smartwatches that can do complex scans like electrocardiograms (ECGs), smart fabrics that can detect blood pressure and predict the likelihood of heart attacks, and smart gloves that can alleviate tremors experienced by Parkinson’s disease patients are just a few examples of the incredible growth of the “Internet of Medical Things” in the past few years. The development of wearable gadgets that can monitor and identify indicators of mental diseases is gaining attention alongside physical sickness. Medical wearables could soon include some of the features shown in research published this year that show how physical markers like sleep patterns, heart rate, and activity levels can be used to determine when someone might be at risk of depression.
4. Health Data Security: AI will strengthen data security by monitoring and identifying potential breaches and unauthorized access to personal health information. These trends indicate the increasing role of AI and ML in personal healthcare, with a focus on enhancing individual health, improving diagnoses, and making healthcare services more accessible and convenient. Staying informed about these developments will be crucial for individuals and healthcare professionals seeking to harness the benefits of AI and ML in healthcare in 2024 and beyond.
5. Remote Patient Monitoring: AI-powered wearables and devices will monitor patients’ health remotely, providing real-time data to healthcare providers for proactive care. Patients with non-emergency ailments may now see their physicians more quickly and inexpensively through remote, live video sessions utilizing their computers or mobile phones. This is due to the restricted access to healthcare staff. Patients can also message their doctors and organizations using telehealth technologies to ask questions about insurance or adjust their prescriptions. Additionally, it facilitates easy access to health education for patients. In addition, a patient’s whole medical history can be more easily accessible with telehealth systems as they enable the integration of data from various patient visits and test findings into electronic health records. Particularly for long-term health issues like hypertension and cardiovascular disease, telemedicine allows patients to stay active participants in their treatment when integrated with data from health wearables.
6. AI-Powered Virtual Assistants: Virtual health assistants will use AI to answer medical queries, provide health advice, and assist with appointment scheduling and medication reminders. Accenture found that 62 percent of those who use healthcare prefer virtual solutions. The same poll also indicated that 57% of people would prefer a way to have their chronic health conditions monitored remotely. Also, for regular checkups, 52% would choose virtual treatment. Given the chance, 42% of customers would “definitely or probably” go for a virtual option, even for disease diagnosis. Babylon Health and other health service companies are now offering new telehealth services.
7. Genomic Medicine: ML algorithms will assist in the interpretation of genomic data, aiding in the diagnosis and treatment of genetic conditions. Medications are usually made with a “one-size-fits-all” mentality, prioritizing maximum effectiveness with minimal adverse effects. Genomic research, digital twins, and artificial intelligence have all enabled doctors to take a more individualized approach, leading to medicines that are a perfect fit for each patient. While it has the potential to alleviate chronic pain effectively, it also carries the risk of side effects when taken in large quantities. Working together, pharmaceutical firms and healthcare facilities can develop individualized diagnostic and therapeutic instruments. Based on particular criteria such as blood sugar levels, personalized therapy offers individualized advice for exercise, nutrition, and disease management. It swiftly results in safe medications for long-term health problems like preventing heart attacks, arthritis, cancer, and Alzheimer’s.
8. Predictive Healthcare Analytics: AI will predict health trends and disease outbreaks, helping healthcare systems prepare for and mitigate health crises. Software like this may do things like remind patients of their next check-ins or find out if people tend to miss their appointments. Hospitals may enhance patient satisfaction and prevent staff overburden by optimizing wait times and staffing based on more accurate patient counts.
9. Drug Discovery: ML-driven drug discovery will accelerate the development of new pharmaceuticals, improving treatment options for various medical conditions. It can take a long time—and a lot of money—for pharma firms to bring a medicine to market. To save time and money, machine learning might sift through mountains of health-related or biological data for previously unseen insights. From the first idea to the final results of a medication study, machine learning is already an integral part of the process.
10. AI in Radiology: AI will continue to enhance radiological diagnostics, aiding radiologists in the detection of diseases and abnormalities with greater accuracy. The resolution of these photographs can be improved by artificial intelligence using upscaling algorithms. The use of image data augmentation techniques allows for the synthetic generation of data for these types of models. To complete the radiology workflow, other AI algorithms may be fed these improved pictures.

Read the Latest blog from us: AI And Cloud- The Perfect Match

Smart Use of Artificial Intelligence in Health Care

The MGI has investigated the potential effects of AI and automation on the future of employment. Healthcare is one of the industries with the least amount of time that might be automated—only 35% of the time, and this varies by job type—but automation will impact most employment across all industries to varying degrees. The possibility of automation and the probability of its implementation are distinct.

The research is based on a middle-ground scenario that predicts that 15% of healthcare workers’ current shifts will be automated. The following graphic displays, for a variety of healthcare vocations in certain European nations, the percentage of working hours that might be eliminated by automation by the year 2030. This doesn’t take into account the possibility of subsequent upheaval due to other variables, such as customization, which can transform healthcare by centering on a “portion of one.”

When it comes to healthcare, how many jobs will be eliminated by AI and automation? The truth is that there is a huge and predicted growing manpower gap in the European healthcare sector. As an example, the present supply of 8.6 million nurses, midwives, and healthcare assistants across Europe will not be enough to fulfill present or expected future demand, according to the World Health Organization, which forecasts overall need for healthcare professionals to climb to 18.2 million across Europe by 2030.4Care providers essential to the daily lives of European people, such as home health aides, licensed practical and vocational nurses, and others, will be in high demand in the future, according to MGI’s demand analysis of healthcare activities. It shows that automation has the potential to help with healthcare manpower shortages, as demand for healthcare jobs is expected to rise. For instance, even though around 10% of nursing tasks might be eliminated by automation, the overall number of nursing employment is projected to rise by 39% by 2030.

More than only employment gains or losses, the workforce will feel the effects of changes to the nature of work itself. Any shift in perspective is a chance to reevaluate and enhance patient care. Up to 70% of a healthcare provider’s time is devoted to mundane administrative duties; AI can assist in eliminating or significantly reducing this burden.

As a result, healthcare education will need to evolve to place more emphasis on creativity, entrepreneurship, lifelong learning, and collaboration across disciplines rather than rote memorization of data. The most significant shift will be the requirement for healthcare companies to include digital and AI capabilities. This includes training frontline employees to use AI in their daily tasks as well as doctors to alter the traditional consultation model. Practitioners, organizations, and systems must all work in tandem to bring about this massive shift in corporate culture and capabilities.

Read: 10 AI ML In Banking And Finances Trends To Look Out For In 2024

The idea that this kind of AI employment would be both popular and controversial-free was a common thread running across the interviews. AI is capable of more. Improved patient outcomes and treatment quality are possible benefits of its ability to supplement various clinical activities and provide healthcare practitioners with access to relevant information. It has the potential to facilitate remote monitoring and patient empowerment through self-care, increase the speed and accuracy of tests, and make more information available to practitioners more quickly and easily.

The post 10 AI ML In Personal Healthcare Trends To Look Out For In 2024 appeared first on AiThority.

A report by Accenture states that AI adoption in the energy sector could result in a 20% increase in energy efficiency by 2035.

What Role Does AI Play in Reshaping the Energy Management Industry?

In recent years, AI has become an increasingly important technology in the energy and power industries. It can automate and optimize a variety of energy-related activities, leading to more efficient and less expensive operations, improved energy management, and less adverse environmental impacts. Demand forecasting is one of the most important areas where AI is being employed in the energy business. Utility businesses may improve resource allocation and management with the help of AI systems that can more precisely predict energy usage by analyzing data on consumer behavior, weather patterns, and other variables.

With the help of AI, the generation and distribution of energy might be optimized.

Machine learning algorithms, for instance, may analyze data from solar or wind power plants to identify patterns and make predictions about future energy production. The sometimes erratic output of renewable energy sources may be more manageable for operators. One of the most important applications of AI in the energy industry is in the field of building energy management. Artificial intelligence-enabled devices may monitor and assess a building’s energy use, identifying wasteful practices and providing recommendations for improvement. This has the potential to save building owners and occupants a lot of money while also reducing their carbon footprint.

Read: Top 15 AI Trends In 5G Technology

Energy Intelligence

Utilities may benefit from artificial intelligence (AI) capabilities like machine learning, natural language processing, and computer vision in a variety of ways, including increased accuracy in demand forecasting, more efficient energy generation and distribution, and faster troubleshooting of malfunctioning machinery. The efficiency and quality of services provided by a facility can be improved while costs are reduced.

Since utilities are under increasing pressure to optimize energy production and distribution to meet rising demand while also ensuring that their systems remain reliable and cost-effective, the market for AI in the energy and power industry has benefited.

The U.S. Energy Information Administration (EIA) estimates that global energy consumption will increase by over 50% between 2018 and 2050.

By improving the grid’s capacity to incorporate renewable energy sources and controlling energy storage and distribution, artificial intelligence can help alleviate a variety of problems related to renewable energy use. This has the potential to improve the power system’s dependability and stability while also decreasing the cost and increasing the sustainability of energy generation.

Read the Latest blog from us: AI And Cloud- The Perfect Match

Top Companies in the Energy Sector

• Alpiq
• SmartCloud Inc.
• General Electric
• Siemens AG
• Hazama Ando Corporation
• ATOS SE
• AppOrchid Inc.
• Zen Robotics Ltd.
• Origami Energy Ltd.
• Flex Ltd.

Top 10 Trends of AI in the Energy Sector

Smart Grids

A smart grid is an idea made possible by the use of AI in energy management. To maximize efficiency in power generation, transmission, and use a “smart grid” incorporates existing power infrastructure with cutting-edge technology such as artificial intelligence.

The International Energy Agency (IEA) estimates that smart grids with AI applications may cut power consumption by 10 percent and greenhouse gas emissions by 15 percent.

AI algorithms can evaluate real-time data from smart meters, sensors, and IoT devices to discover abnormalities, forecast equipment breakdowns, and optimize energy flow. AI helps utilities find that sweet spot between supply and demand by smartly regulating energy distribution. Less energy wasted and a marked improvement in the effectiveness of the grid as a whole. Artificial intelligence is about to have a profound effect on the energy management industry.

Microgrids

Microgrids are smaller versions of electricity grids that may function autonomously from the larger, more central grid. Artificial intelligence and machine learning are utilized by microgrid control systems to regulate energy flow and maximize efficiency. Microgrids are gaining popularity due to their ability to integrate renewable energy sources into the energy grid and offer backup power in the event of an outage.

Detecting Energy Theft and Fraud

As much as $6 billion is lost annually in the United States due to electricity theft and fraud in the energy and utilities sector.

Energy theft occurs when someone illegally takes power from the grid. Misrepresenting energy statistics or use is considered energy fraud. Automated anomaly detection with AI and ML can alert utilities to potential problems. In doing so, energy providers may safeguard their assets, cut down on unnecessary energy use, and pocket the savings.

Grid Management Energy Efficiency and Demand Response

Sustainable energy management relies heavily on improving energy efficiency, and AI is crucial in this regard. Artificial intelligence systems can analyze consumption habits and construct energy models to pinpoint inefficiencies and provide solutions to cut down on waste.

Artificial intelligence makes possible demand response schemes that pay people to reduce their energy use during high-demand times. Consumers may help alleviate grid congestion and support a cleaner energy environment by using AI-enabled smart devices and home automation systems to engage in demand response efforts.

Read: AI and Machine Learning Are Changing Business Forever

Energy Trading

Due to the time-sensitive nature of energy delivery, trading in energy is distinct from trading in other commodities. For energy dealers, this poses a difficulty but also an opportunity since the energy market is becoming more liquid. Predicting energy demand and giving traders real-time information about energy pricing are two ways in which AI and machine learning might improve the efficiency of the energy trading market.

Energy brokers can use this data to better time their purchases and sales of energy. Power purchase agreements (PPAs) are a new kind of financial contract that may be executed on the blockchain. The adoption of blockchain technology improves the effectiveness of these contracts since it speeds up transactions, lowers associated costs compared to more conventional PPA platforms, and is built on a more robust and reliable infrastructure.

Grid Safety

Because of its complexity, the electricity infrastructure is susceptible to cyberattacks.

By thwarting cyberattacks in advance, AI and machine learning can make power systems safer for everyone. Data analytics is used to look for indicators of a cyberattack in energy usage data. Artificial intelligence and machine learning can be used to counteract cyberattacks once they have been detected.

Read 10 AI In Manufacturing Trends To Look Out For In 2024

Predictive Analytics

AI’s use of predictive analytics is a significant addition to the field of energy management. Predicting energy consumption patterns, weather conditions, and equipment performance are all areas where AI systems thrive through the analysis of massive amounts of historical and real-time data.

A recent report predicts that global AI in the energy sector is expected to reach $7.78 billion by 2025, driven by the increasing adoption of predictive analytics.

For instance, utilities may improve electricity generation and distribution by using AI algorithms to forecast peak energy demand. In addition to saving money, this improves the reliability of the power grid. AI aids energy suppliers in making wise choices and optimizing resource allocation through precise predictions of energy usage.

Customer Engagement

AI and ML are being put to use for the first time in the energy industry to improve interaction with customers. Companies in the energy industry may better serve their consumers’ demands by applying AI and machine learning. Data analytics are used to learn about customers’ energy consumption patterns, and those patterns are then used to tell consumers about how they might cut their energy use through behavioral changes.

Boosted Output

The energy industry is likewise making use of AI and ML to boost output. Machine learning algorithms are being used by the oil and gas industry, for instance, to optimize well location and boost output. Companies may make more informed judgments about where to drill for oil and gas by analyzing data gathered from seismic surveys and other sources. This will improve energy efficiency while also making the electricity grid simpler and more efficient.

Energy-Storage Devices

By 2030, the energy storage industry is expected to have expanded by a factor of 20. Integrating smart energy storage devices into the electric grid is a step toward more effective energy management. Virtual power plants, which are made possible by energy storage and allow utilities to meet peak demand even when supplies are low, are another example of this trend. As a result, fewer new power plants will need to be constructed by the energy industry.

The post 10 AI In Energy Management Trends To Look Out For In 2024 appeared first on AiThority.

As we embark on the financial landscape of 2023, Mastercard takes center stage with a series of groundbreaking developments that reshape the future of digital payments and financial technology. Mastercard, a global leader in payment solutions, kicks off the year 2023 with a slew of transformative news stories, highlighting its pivotal role in driving innovation and shaping the way we conduct transactions.

In the dynamic world of finance, Mastercard stands as a beacon of innovation, and the top 10 news stories for the year 2023 offer a glimpse into the company’s strategic maneuvers and technological advancements. As the digital economy continues to thrive, Mastercard unveils a tapestry of significant news in 2023, showcasing its commitment to revolutionizing the way we approach payments, security, and financial inclusion.

From cutting-edge advancements in contactless technology to strategic partnerships fostering financial inclusivity, Mastercard’s top 10 news stories for 2023 encapsulate a narrative of progress and adaptability in the ever-evolving realm of global finance.

Top 10 News of Mastercard in 2023

Dynamic Yield by Mastercard Unveils Shopping Muse, the Next Generation Personal Retail Assistant

The post Top 10 News of Mastercard in 2023 appeared first on AiThority.

Hi Steve, welcome to the AiThority Interview Series in 2023. Please tell us about your two decades of tech experience so far. How did you arrive at Mastercard?

My career has had several phases. For the first 10 years or so after graduating, I worked at various – mostly small – independent, software companies and consultancies. My position evolved over the years; I started as a developer before advancing to the role of a software engineering manager and then eventually becoming a CTO.

Next, I worked for Science Foundation Ireland (SF), Ireland’s national science funding agency, where I led our investments in topics such as computer science, data science, software engineering, and artificial intelligence.

In 2014 I started at Mastercard, which is where I still currently work today. Initially, I supported and grew a team called Start Path, an engagement program for innovative startups in the fintech space. A few years later, I joined Mastercard Foundry, the innovation and R&D arm within the company, leading research and development for AI, ML, and now also quantum computing. This July, I was appointed to Mastercard’s first class of Distinguished Engineers, a recognition for select Senior Vice President technical experts as part of the company’s continuing commitment to technology, innovation, and career growth. With this distinction, I continue my work with a focus on artificial intelligence and quantum computing. 

You are in charge of Mastercard R&D’s strategy and execution of AI and ML in new product development efforts. What is the biggest challenge to Digital Transformation in the market you cater to?

Of the many years that I’ve worked in technology – this current period is distinct for the speed and scale of innovation taking place. This dynamism is exciting because we’ve only just scratched the surface of what is possible for businesses and consumers, but with it also comes new challenges for enterprises.

For one – leveraging emerging technologies to build new products and services.

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Part of the equation is to understand and introduce technologies like web3, spatial computing, PETs, and AI/ML, while also maintaining and upgrading legacy systems, and aligning with ever-evolving legislation and regulation governing their application.

AI specifically has been top of mind for our market, especially following the fairly recent explosion of generative AI.  Mastercard has been putting AI to work for years, particularly in our products and solutions across open banking, routing, personalization, and fraud that enhance the safety and security of the payments ecosystem.  Although the step change between AI and generative AI is exponential in terms of what you can do with it, our deep roots in AI have afforded us the capabilities, talent, framework, and partnerships to keep a pulse and execute on emerging technologies.  

As a leader in the payments space, and as with any nascent technology, Mastercard has a responsibility to set the precedent for exploring generative AI responsibly.  We developed an AI governance program and guidelines for our data scientists to minimize risks in AI and best serve our customers, invested in partnerships with key institutions like RIT In Dubai and Howard University, and actively encouraged our employees to safety test and learn. 

What technologies within AI and computing are you interested in?

The idea of being able to control a computer system and anything connected to it through programming has fascinated me since I was a teenager.

Today I’m looking at how AI, mixed reality, spatial computing, and web3 have unlocked an entirely new frontier in technology. We’re likely to see several key trends, such as the rapid increase in computational power, both at the edge and in the cloud, and the tokenization of assets to start to coalesce around some of these new computing paradigms.

For AI, the incredible advances born from generative AI and Large Language Models (LLMs) are also contributing to the transformative period we’re in.

Currently, Mastercard is engaging in test-and-learn with generative AI applications to enhance operational efficiency and improve data quality, aggregation, entity resolution, and categorization.

We’re also using ML for certain models that support our open banking solutions, such as credit scoring, financial management insights, account opening, and payments. It enables us to extract, identify, and classify data quickly and more efficiently than rules-based models alone.

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In the longer term, I’m paying close attention to both quantum computing and AGI. With quantum, I’m tracking developments in both hardware and software, to understand how these new devices will help us to solve ever more complex computational problems, and what use cases will arise in our industry. With AGI, at some stage, we may be looking at the prospect of human-like machines that can solve a wide range of complex tasks at scale. 

In the current analysis, it is reported the global quantum computing (QC) market will be at $900 million. How do you see QC disrupting the digital market in the next couple of years? 

At $900m, quantum computing is still a very small part of the overall computing market.

Over the next few years, we’ll see quantum computers – inclusive of quantum annealers – get progressively more performant, capable, and reliable.

Currently, our best guess is that the earliest use cases in banking and payments will most likely be in the optimization space, with other applications such as machine learning coming later.

Rather than a disruption, it’s more probable that we’ll see quantum technology adopted gradually, across industries and companies, as the technology continues to improve, becomes more usable, and its primary use cases become more evident. 

What steps can young technology professionals take to enhance their proficiency in collaborating effectively with Cloud, Automation, and AI-based tools? 

Nothing beats getting “hands-on-keyboard” experience using these technologies.  One of the amazing benefits that all young tech professionals have today is readily available online learning materials. There are tutorials on YouTube for just about every emerging technology imaginable, and through cloud computing, there’s also access to the resources required to explore those areas. Many cloud and tech companies also offer cheap or free trial accounts to help young developers learn their technologies at little or no cost.

On the Mastercard Developers platform, for example, you’ll find a quick start guide that will walk you through how to create a new project using Mastercard’s APIs, and gain access to the Sandbox environment. So, armed with nothing more than a laptop and an internet connection, people can get access to all the technology they could imagine, even quantum computers!

One of the tried and tested ways to build skills in these areas has been through the open-source community – whether it’s contributing to an existing project that you find interesting or relevant, or starting a personal project that scratches your own itch. 

What are your predictions for AI/ML and other smart technologies heading beyond 2024?

As machine and deep learning evolve, so too will their role within our sector. This past year has been about experimentation. In 2024, we expect generative AI to continue to gradually integrate into business operations and products.

Companies are currently focused on internal generative AI applications, like software development co-pilots, knowledge bots and operational efficiency drivers that are serving as testbeds and laying the groundwork for what’s to come. This phase is likely to continue throughout the year, as companies start building the foundations for implementation. As challenges like data privacy, information accuracy and bias are addressed, we anticipate that the range of use cases will expand to include more ambitious and public-facing deployments.

 

One of the most compelling use cases for generative AI in the financial services industry is in open banking. With the aid of fine-tuned LLMs, generative AI can enable the cleaning and categorization of data at a significantly higher through-put and with more accuracy than previously available.

In line with informed data consent protocols, generative AI could streamline personal financial management, for example, by acting as a personal wealth manager to create an encompassed view of an individual’s financial well-being, help formulate college savings plans, procure loans and implement financial strategies – empowering people to navigate their financial lives more adeptly.

Thank you, Steve! That was fun and we hope to see you back on AiThority.com soon.

• About Steve
• About Mastercard

About Steve

Steve is an IT professional with more than 25 years of industry experience in payments, government, and academia. He is currently responsible for leading Mastercard Foundry’s R&D initiatives in emerging technologies, including artificial intelligence, machine learning, quantum computing, 5G and Web3. In this role, Steve leads a team of talented data scientists, data engineers and software engineers to bring new products and services to market.

About Mastercard

Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments and businesses realize their greatest potential. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.

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What AI can’t do is replace the natural ‘gut feel’ of a healthcare professional.

Smart Use of Artificial Intelligence in Health Care- How Robots are Helping Doctors?

With the help of AI, telemedicine and RPM may provide patients with up-to-the-minute health information. To guarantee ethical AI use, legislative frameworks, and ethical concerns have come to the fore. Instead of seeing AI as a replacement for human healthcare providers, the focus is on how AI systems may work in tandem with them. The picture highlights the revolutionary integration of AI into several aspects of healthcare, which holds great promise for better diagnoses, personalized treatments, and overall patient care.

By 2024, artificial intelligence is expected to make great strides in the medical field. Deep learning algorithms improve the accuracy of X-ray, MRI, and CT scan interpretation, which is useful in medical imaging and diagnostics. AI plays a crucial role in the drug discovery process by sifting through large datasets in search of promising compounds and automating development. Using a patient’s unique genetic and molecular profile, personalized medicine employs AI to create individualized treatment plans. Using natural language processing to glean insights from unstructured clinical data, predictive analytics helps with patient outcome prediction and at-risk population management.

The research examines six key areas where AI directly affects the patient and three sectors of the healthcare value chain that might gain from more scaling of AI. It also looks at specific instances of current AI solutions in healthcare.

The increase in investments isn’t surprising, as 90% of the healthcare leaders surveyed believe that AI initiatives are important for their organizations to remain competitive in the market. When asked about their organization’s approach to technology innovation, 80% self-reported that they are either edge experimenters (organizations that tend to be first adopters of new technology or first to try new approaches and test unknown use cases) or fast followers (organizations that typically are next in line to adopt after some experimentation).

Robots Cannot Substitute Doctors!

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An unprecedented gathering of AI pioneers, experts, and fans, the Global AI Conclave 2023 was co-hosted by CNBC-TV18 and Moneycontrol. On December 16, the JW Mariott in Bengaluru hosted the conclave, which had 15 or more sessions moderated by prominent figures in artificial intelligence (AI) from India and throughout the world. So far, this is what has transpired throughout the event.

The New Delhi proclamation, which is a component of the Global Partnership on Artificial Intelligence (GPAI), has been enthusiastically supported by representatives from twenty-eight nations. Taking advantage of AI while minimizing its drawbacks is the main goal. The declaration brings attention to several AI-related issues, such as the following: fighting disinformation and misinformation; resolving concerns about unemployment; guaranteeing transparency and fairness; protecting intellectual property and personal data; and avoiding dangers to human rights and democratic principles.

Read: Top 10 Benefits Of AI In The Real Estate Industry

Prominent Personalities At The Conclave

• Andrew Ng, one of the most influential voices in the world of artificial intelligence
• Rajeev Chandrasekhar, the Union Minister of State for Electronics and Information Technology, India.
• Sridhar Vembu (Founder & CEO, Zoho)
• Sharad Sharma (Co-founder, iSPIRT Foundation)
• Swapna Bapat, VP of Product Marketing at Palo Alto Networks
• Andrew Feldman, Founder & CEO of Cerebras Systems
• Vivek Raghavan, Founder of Sarvam AI
• Chris Miller, author of Chipwar

Andrew Ng Thoughts

According to Andrew Ng, a prominent figure in the field of artificial intelligence, India has all the makings of a world leader in the emerging but quickly expanding industry. The country’s high rate of AI skill penetration and the immense interest in the technology are the reasons behind this.

Among the 90,000 students enrolled in Coursera’s generative AI course in the first month, Ng stated that 20,000 came from India, making it the country with the second-fastest enrollment rate. According to him, this shows how much and how concentrated the interest in artificial intelligence is in the country.

Read 10 AI In Manufacturing Trends To Look Out For In 2024

Ng further referenced a report from Stanford University’s AI Index that, according to data compiled by LinkedIn, ranks India as the country with the greatest penetration rates of AI skills, surpassing even the US.

While information is still being spread at a rapid pace, Ng—who had previously established the Google Brain research lab in 2011—mentioned that this has led to numerous other pockets of quickly increasing talent in nations like the UAE, China, France, and the UK.

Rajeev Chandrasekhar Thoughts

While pleading for a worldwide framework that governs AI without demonizing it, Rajeev Chandrasekhar, the Union Minister of State for Electronics and Information Technology, referred to artificial intelligence as the “greatest invention of our time” on Saturday, December 16. After ChatGPT, there was a noticeable movement toward a more nuanced and safety-centric approach, which Chandrasekhar emphasized as a seismic shift in world attitudes. To use AI for good while avoiding its dangers is a fine balancing act, which is in line with Chandrasekhar’s opinion.

India’s Role in Promoting Collaborative AI

Everyone in the AI community and beyond was enthused by the success and impact of the 2023 Global AI Conclave. In addition to discussing AI-related topics and prospects, the conclave offered some specific, doable suggestions for future collaboration and action. Important points discussed during the conference included:

AI has the potential to be a game-changer, but it also presents certain dangers and difficulties that must be handled with caution and responsibility.
– An interdisciplinary and stakeholder-driven strategy is necessary because there is no silver bullet in this varied and ever-changing area.
-When implemented properly and fairly, artificial intelligence (AI) is not a zero-sum game, but rather a win-win opportunity that may benefit everyone.
– This is not some faraway idea; rather, it is a concrete, actual thing that has an impact on and influences our daily lives and choices.

An important turning point in the history of artificial intelligence (AI) innovation and international cooperation was the 2023 Global AI Conclave, which took place in India. At the conclave, it was clear that India is more than just a user of artificial intelligence; the country is also at the forefront of AI development and has something special to offer the international community. Inspiring and motivating attendees and participants joined the AI revolution and shape the future they see, the conclave left a lasting impression.

Read: 4 Common Myths Related To Women In The Workplace

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How Can AI Fit Into the Field of Neonatology?

Preterm births account for about 10% of live births, and almost all preterm newborns have trouble feeding. The number of newborns in the United States who experience difficulty feeding is increasing, and it currently stands at over 2.8 million.

In neonatology, artificial intelligence is currently being tested for a variety of purposes, such as monitoring vital signs, diagnosing and prognosing neurological disorders, and predicting diseases such as respiratory distress syndrome, bronchopulmonary dysplasia, apnea of prematurity, retinopathy of prematurity, intestinal perforation, and jaundice.

A Review of NICU Data Quality

Think about a typical neonatal intensive care unit (NICU): the tempo of care is high, and practically all clinical decisions are made in real-time. The lack of high-quality data is already making those judgments difficult. Despite the wealth of clinical data stored in EHRs, the unstructured data that NICU teams require is frequently obscured, absent, or filled with errors in patient records.

A NICU nurse, for example, can probably tell if a dosage of medication has been inadvertently increased by zero based on a baby’s weight and can immediately account for the mistake because the human eye can fix many of these mistakes. That AI will act similarly is still a mystery to humans. Already, clinicians’ mistrust and the excessive amount of rework caused by poor data quality in NICUs are out of control.

It’s adding fuel to the fire of burnout among nurses who feel a moral imperative to verify every detail in a patient’s chart, even if they rarely have the opportunity to do so. Assuming the AI systems are indeed developed using pediatric data, NICUs are not in a position to responsibly rely on them for any part of patient care.

There is a dearth of pediatric data in current AI research, which causes researchers to draw incorrect conclusions about pediatric populations from adult datasets, as pointed out in a new framework of guidelines for the responsible use of pediatric data in AI studies. It is often believed that NICUs should immediately begin using AI to improve patient outcomes and decision-making. However, health systems would be better served by exercising prudence and bolstering the quality of their clinical data in NICUs. You get out of life what you put into it, as the adage goes.

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