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Artificial Intelligence Moves Heavily Into the NFT Space!

Investors take note, machine learning is beginning to have a powerful impact on generative art non-fungible tokens (NFTs).

NFTs are cryptographic assets on a blockchain with unique identification codes and metadata that distinguish them from each other. Most often, NFTs are used to represent real-world items like artwork and real estate. “Tokenizing” these real-world tangible assets makes buying, selling, and trading them more efficient while reducing the probability of fraud.

With that in mind, AI is becoming increasingly important in the non-fungible token space. “Generative art” – art that has been created by AI — has quickly emerged as one of the main categories of the NFT market, driving innovative projects and investment in astonishing collections. From the works of AI art legends such as Refik Anadol or Sofia Crespo to Tyler Hobbs’s new QQL project, NFTs have become one of the main vehicles to access AI-powered art.

The rise of generative AI has come as a surprise even to many of the early AI pioneers, who mostly saw this discipline as a relatively obscure area of machine learning. Its leap to dominance in the NFT market has largely been driven by gains in computational power and next-gen MI algorithms that can help models learn without requiring a lot of labeled datasets, which are incredibly limited and expensive to build.

One of the most significant of these advances has been “text to image” (TTI). AI-driven TTI programs such as DALL-E and GLIDE allow users to describe in text what they would like the program to render, and it then creates an interpretive image based on the text, with some profoundly remarkable results. Thus making TTI ideal for the creation of unique and marketable NFTs.

There are also similar generative art solutions such as “text-to-video” or “image-to-image,” but TTI, by far, is having the greatest impact on the NFT market because a disproportionate percentage of digital art collectibles are represented as static images.

Throughout the history of technology, there have been many examples of seemingly disparate trends coming together to form a market symbiosis that benefits both. The most recent example is the social-mobile-cloud revolution, in which each one of those trends expanded the market of the other two.

Generative AI and NFTs are starting to exhibit a similar dynamic. Both trends have been able to bring complex technology to mainstream culture. NFTs complement generative AI with digital ownership and distribution models that would be nearly impossible to implement otherwise. Similarly, generative AI is likely to become one of the most important sources of NFT creation now and into the future.

How BigRio Helps Facilitate Investment in AI Startups

Like what is occurring with NFT and AI-generated art, BigRio looks for and helps to facilitate such market symbiosis.

We like to think of ourselves as a “Shark Tank for AI.”

If you are familiar with the TV series, then you know that, basically, what they do is hyper-accelerate the most important part of the incubation process – visibility. You can’t get better visibility than getting in front of celebrity investors and a TV audience of millions of viewers. Many entrepreneurs who have appeared on that program – even those who did not get picked up by the sharks – succeeded because others who were interested in their concepts saw them on the show.

At BigRio, we may not have a TV audience, but we can do the same. We have the contacts and the expertise to not only weed out the companies that are not ready, as the sharks on the TV show do but also mentor and get those that we feel are readily noticed by the right people in the growing AI investment community.

Because we see so many potential AI innovators, we are also ideally suited to create the kind of synergy between concepts and applications such as is occurring with AI, ML and NFTs.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

 

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AI Leverages Nursing Expertise to Improve Patient Outcomes

The know-how and experience of nurses are a critical body of knowledge and expertise about patients and patient care. Wouldn’t it be wonderful if there was a way to pool all of that knowledge into one place for hospitals and health organizations to make better decisions about patients?

There is, and as you might imagine, it involves AI.

Several health systems, led by the Columbia University Irving Medical Center (CUIMC), are testing an AI-driven predictive tool that is attempting to emulate nurses’ seemingly innate ability to pick up cues about patients’ health from subtle changes in behavior and appearance.

According to its developers, CONCERN (COmmunicating Narrative Concerns Entered by RNs) is a predictive tool that extracts nurses’ expert and knowledge-driven behaviors within patient health records and transforms them into observable data that support early prediction of organ failure or other critical conditions in hospitalized patients.

CUIMC is partnering with three hospital systems — Mass General Brigham (MA), Vanderbilt University Medical Center (TN), and Washington University School of Medicine/Barnes-Jewish Hospital (MO) — to test the effectiveness of the CONCERN implementation toolkit, developed to support large-scale adoption of the tool.

This initiative recently received funding from the American Nurses Foundation through the Reimagining Nursing Initiative.

“CONCERN shows what nurses already know: Our risk identification is not simply a subjective clinical hunch,” said Sarah Rossetti, assistant professor of biomedical informatics and nursing at Columbia, in a statement. “We’re demonstrating that nurses have objective, expert-based knowledge that drives their practice, and we’re positioning nurses as knowledge workers with tremendous value to the entire care team.”

Annually, more than 200,000 patients die in US hospitals from cardiac arrest, and over 130,000 patients’ deaths are attributed to sepsis. Many of these deaths could be preventable if patients who are at risk are detected earlier. Prior work from the CONCERN team found that nursing documentation within EHRs contains information that could contribute to early detection and treatment, but these data are not being analyzed and exposed by EHRs to clinicians to initiate interventions quickly enough to save patients.

How BigRio Helps Bring Advanced AI Solutions to Healthcare

Like the CONCERN project, leveraging human expertise and adapting to the predictive power of AI algorithms is an area where AI and machine learning are making one of the technology’s biggest impacts in the healthcare field.

BigRio prides itself on being a facilitator and incubator for such advances in leveraging AI to improve patient outcomes. In fact, it was my father’s own battle with and eventual death from lung disease that set me on my path to finding ways to use AI to provide earlier detection of serious medical conditions for improved patient care.

Eventually, among our other success stories, we did collaborate with a researcher who is in the process of developing a cognitive digital twin of the human lung. Right now, that technology is being used specifically in the realm of testing inhalers for asthma patients, but like the CONCERN project, it has broader implications for better diagnostics and early interventions to save lives.

We like to think of ourselves as a “Shark Tank for AI.”

If you are familiar with the TV series, then you know that, basically, what they do is hyper-accelerate the most important part of the incubation process – visibility. You can’t get better visibility than getting in front of celebrity investors and a TV audience of millions of viewers. Many entrepreneurs who have appeared on that program – even those who did not get picked up by the sharks – succeeded because others who were interested in their concepts saw them on the show.

At BigRio, we may not have a TV audience, but we can do the same. We have the contacts and the expertise to not only weed out the companies that are not ready, as the sharks on the TV show do but also mentor and get those that we feel are readily noticed by the right people in the biomedical community.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

 

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AI Creates Novel Proteins Paving the Way for Radical New Therapies!

An exciting partnership between computing and graphics giant Nvidia and AI startup Evozyne has announced that they have been able to produce novel versions of a human protein that has never been seen in nature — but with “enhanced function” and the same safety as native proteins. The researchers say that the AI breakthrough lays the groundwork for potential new therapies for rare disorders.

The collaborators say they developed the novel proteins by “listening to the language of life.”

Natural language processing, or NLP, is a cornerstone of AI programs. NLP is software that is designed to analyze language in any form, from handwritten notes to complex peer-reviewed papers. In healthcare, such NLP algorithms are being written that can analyze any kind of document and other datasets and identify biologically relevant text elements such as the names of genes, proteins, drugs, clinical manifestations of a particular disease, and anything else relevant to a given research team’s target.

Kimberly Powell, vice president of healthcare at Nvidia, says natural language learning techniques can also be applied to genomics, in particular proteins that are encoded by our genes.

“We’ve learned that these large language models can understand relationships just through studying amino acid sequence,” Powell said. “There is information about protein function when you can encode and represent and explore that data in these large language models.”

The results of the collaboration between Nvidia and Evozyne were announced during the recent J.P. Morgan Healthcare Conference in San Francisco. Powell discussed them during a separate briefing with journalists. Neither company develops new drugs, but their technologies are used by biotech and pharmaceutical companies working in drug discovery.

The two companies began working together in 2022, collaborating to develop a new deep-learning model that can learn the rules of protein function. Using those rules, they aimed to design new proteins with improved functions. The model was built on Nvidia’s technology for training and deploying large language models for biology.

When engineering therapeutic proteins, scientists aim to make changes that enhance the protein’s function without compromising its safety. In the research described by Nvidia, Evozyne was able to create a protein with 51 mutations. Despite all of those changes, that protein was still able to achieve a two-and-a-half times enhancement in functionality compared to the native human protein on which it was modeled.

In this case, the protein model was specifically designed to test novel therapeutics for a rare condition known as phenylketonuria (PKU), in which phenylalanine levels build up in the body and cause neurological impairment because individuals with the condition cannot process phenylalanine, an amino acid found in certain foods.

Right now, there are few drugs available to treat PKU, and those with the condition must be on strict diets avoiding foods that contain phenylalanine.

How BigRio Helps Facilitate Investment in AI Startups

Much like the collaboration between Nvidia and Evozyne, BigRio is partnering with startups to develop healthcare solutions with AI at the core of their solutions and our sister company Citadel Discovery was launched in 2021 with the purpose of giving a kind of “open access” to the data and technology that will drive the future of pharma research streamlining and lowering the costs of drug discovery and biological research.

We have launched an AI Studio specifically for US-based Healthcare startups with AI centricity. Our mission is to help AI startups scale and gear up to stay one step ahead of the pack and emerge as winners in their respective domains.

AI Startups face numerous challenges when it comes to demonstrating their value proposition, particularly when it comes to advanced AI solutions for pharma and healthcare. We have taken an award-winning and unique approach to incubating and facilitating startups that allow the R&D team and stakeholders to efficiently collaborate and craft the process to best suit actual ongoing needs, which leads to a faster, more accurate output.

We provide:
• Access to a top-level talent pool, including business executives, developers, data scientists, and data engineers.
• Assistance in the development and testing of the MVP, Prototypes, and POCs.
• Professional services for implementation and support of Pilot projects
• Sales and Marketing support and potential client introductions.
• Access to private capital sources.

BigRio excels in overcoming such initial hurdles, which present nearly insurmountable obstacles to a startup operation.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

 

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What is Machine Perception, and Why is It important to AI?

Machine perception is that aspect of digital technology that involves developing computers that can sense or “perceive” the outside world in a way that accurately mimics the five human senses – sight, sound, touch, smell, and taste — as well as taking in information in ways that humans cannot.

As you might imagine, machine perception is an integral part of machine learning and AI, particularly in AI applications that require fast decision-making based on perceiving the surrounding environment, such as autonomous driving.

By definition, perception is the process by which sensory information is captured from the world around us and then interpreted, understood, and organized to make decisions based on the input of the sensory data. In humans, that data is obtained by our sensory organs, such as our eyes, ears, skin, nose, and tongue. These specialized organic receptors transmit their information across neural pathways to the brain to organize and interpret the data they obtain.

In machine learning and AI, a variety of digital sensors are used to replicate or augment the human sense organs. These sensors then work with a complex network of hardware and software that is, in essence, a digital parallel of the nervous system to create “machine perception.”

Machine perception is a cornerstone of every AI sensory model or cognitive digital twin application. The algorithms convert the data gathered from the world into a raw model of what is being perceived by the AI or the twin.

In theory, any direct, computer-based gleaning of information from the world is a kind of machine perception. That is anything from the photoelectric sensors that automatically turn on your car’s headlights at night to how your Roomba vacuum navigates around your living room. Right now, AI and machine perception applications are in development that are designed to emulate each of the human senses, such as:

• Machine or computer vision via optical camera

• Machine hearing (computer audition) via microphone

• Machine touch via tactile sensor

• Machine smell (olfactory) via electronic nose

• Machine taste via electronic tongue

• 3D imaging or scanning via LiDAR sensor or scanner

• Motion detection via accelerometer, gyroscope, or magnetometer

• Infrared and thermal imaging sensors

From this list above, you can see how critical machine perception can be to AI applications such as medical diagnoses, as well as developing truly safe and ubiquitous autonomous vehicles. Innovation in machine perception will also pave the way for next-generation “robotic assistants” or companions.

But developing machine perception is not as easy as it may seem. Computers may be able to solve complex equations and process data vastly superior to humans; however, there are some things that humans still do a lot better than machines. Perception and acting quickly and spontaneously on data from our senses is one of them. Things we do with ease are proving to be very hard to “teach” computers. Take, for example, handwritten text. Handwriting varies greatly from individual to individual, yet, we all can pick up and read handwritten text with no problem for the most part, yet it is difficult to get AI to decern those variables in letter composition.

Similarly, a two-year-old can learn to catch a tossed ball after only a few attempts. But teaching a robot to do the same takes a lot more work. That’s because we are not even sure of the infinite combinations of data processing that almost instantaneously take place as your eyes perceive a ball coming towards you and your brain puts your hand up in time to catch it.

In the 1980s, Hans Moravec, famed member of the Robotics Institute of Carnegie Mellon University in Pittsburgh, described the paradox this way, “It is comparatively easy to make computers exhibit adult-level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility.”

However, such limitations will likely be more easily overcome as AI, and machine learning begin to peel away from conventional computers and enter the realm of quantum computing.

Quantum computing holds the promise of exponentially improving the way AI algorithms process, analyze and present sensory findings and predictions and may hold the key to bringing machine perception more analogous to the functionality of the human brain and nervous system.

A number of companies — startups as well as established challengers — are working to make their AI models perceive the world more as humans do, and BigRio is helping to enable much of this advancement.

How BigRio Helps Facilitate Advancement in Machine Learning

Like breakthroughs in machine perception, BigRio looks for and helps to facilitate such innovation in machine learning and AI.

We like to think of ourselves as a “Shark Tank for AI.”

If you are familiar with the TV series, then you know that, basically, what they do is hyper-accelerate the most important part of the incubation process – visibility. You can’t get better visibility than getting in front of celebrity investors and a TV audience of millions of viewers. Many entrepreneurs who have appeared on that program – even those who did not get picked up by the sharks – succeeded because others who were interested in their concepts saw them on the show.

At BigRio, we may not have a TV audience, but we can do the same. We have the contacts and the expertise to not only weed out the companies that are not ready, as the sharks on the TV show do but also mentor and get those that we feel are readily noticed by the right people in the growing AI investment community.

Because we see so many potential AI innovators, we are also ideally suited to facilitate advancements in machine learning, such as improved machine perception, that will usher in a new generation of autonomous vehicles and other smart machines.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

 

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IT Experts Predict The Top Five Advances in AI for 2023!

AI is known for its ability to make very accurate predictions. But often, human prognosticators are pretty good at it too! In this article, we take a look at what leading IT experts say they think will be the top five advances in AI and machine learning in 2023, as compiled by The Enterprisers Project.

1. There Will Be Continue Advancement of AI Applications in Healthcare

“AI will yield tremendous breakthroughs in treating medical conditions in the next few years. Just look at the 2021 Breakthrough Prize winner Dr. David Baker. Dr. Baker used AI to design completely new proteins. This ground-breaking technology will continue having huge ramifications in the life sciences, potentially developing life-saving medical treatments for diseases like Alzheimer’s and Parkinson’s.” — Michael Armstrong, Chief Technology Officer, Authenticx.

2. Continued Merging of AI and Quantum Computing

Phil Tee, Co-founder, and CEO, of Moogsoft, says to, “Watch the crossover from fundamental physics into informatics in the guise of quantum and quantum-inspired computing. While I’m not holding my breath for a practical quantum computer, we will see crossover. The mix of advanced mathematics and informatics will unleash a new generation of engineers uniquely placed to exploit the AI wave.”

3. AI Will Not Replace Humans

Despite dozens of sci-fi movies and novels to the contrary, the experts do not believe that AI will replace humans in 2023 and the years ahead, but instead, they expect to see increased interaction between human and artificial intelligence with an increased synergy between the two. “While there will be growing adoption of AI to enhance our collective user experience at scale, it will be balanced with appropriate human intervention. Humans applying the insights provided by AI will be a more effective combination overall than either one doing it alone. How and where this balance is struck will vary depending on the industry and the criticality of the function being performed. For example, radiologists assisted by an AI screen for breast cancer more successfully than they do when they work alone, according to new research. That same AI also produces more accurate results in the hands of a radiologist than it does when operating solo.” – E.G. Nadhan, Global Chief Architect Leader, Red Hat

4. A Move Towards More Ethical AI and an AI Bill of Rights

As we reported earlier this year, the Biden administration had launched a proposed “AI Bill of Rights” to help ensure the ethical use of AI. Not surprisingly, it is modeled after the sort of patient “bills of rights” people have come to expect as they interact with doctors, hospitals, and other healthcare professionals.
David Talby, CTO of John Snow Labs, says to see continued movement in this direction. “We can expect to see a few major AI trends in 2023, and two to watch are responsible AI and generative AI. Responsible or ethical AI has been a hot-button topic for some time, but we’ll see it move from concept to practice next year. Smarter technology and emerging legal frameworks around AI are also steps in the right direction. The AI Act, for example, is a proposed, first-of-its-kind European law set forth to govern the risk of AI use cases. Similar to GDPR for data usage, The AI Act could become a baseline standard for responsible AI and aims to become law next Spring. This will have an impact on companies using AI worldwide.”

5. AI Will Support Increased and “Smarter” Automation

“Everyone understands the value of automation, and, in our software-defined world, almost everything can be automated. The decision point or trigger for the automation, however, is still one of the trickier elements. This is where AI will increasingly come in: AI can make more intelligent, less brittle decisions than automation’s traditional ‘if-this-then-that’ rules.” – Richard Whitehead, CTO, and Chief Evangelist, Moogsoft.

How BigRio Helps Facilitate the Future of AI

At BigRio, we not only agree with these experts on these top five advances in AI that will likely occur in 2023, but we are also actively trying to facilitate them!
We like to think of ourselves as a “Shark Tank for AI.”

If you are familiar with the TV series, then you know that, basically, what they do is hyper-accelerate the most important part of the incubation process – visibility. You can’t get better visibility than getting in front of celebrity investors and a TV audience of millions of viewers. Many entrepreneurs who have appeared on that program – even those who did not get picked up by the sharks – succeeded because others who were interested in their concepts saw them on the show.

At BigRio, we may not have a TV audience, but we can do the same. We have the contacts and the expertise to not only weed out the companies that are not ready, as the sharks on the TV show do but also mentor and get those that we feel are readily noticed by the right people in the growing AI investment community.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

The Enterprisers Project is a community and online publication helping CIOs and IT leaders solve problems and drive business value. The Enterprisers Project, supported by Red Hat, also partners with Harvard Business Review.

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Digital Twin Technology Being Used to Create Next-Gen Aircraft

Digital twin technology is becoming more and more mainstream, particularly when it comes to the maintenance and monitoring of complex mechanical systems such as aircraft.

GENEX, an EU-funded startup, has announced the creation of a new digital twin framework that will be used to create “the next generation” of safer and greener composite aircraft.

Launched on Sept. 1, 2022, GENEX is a 42-month, 5.7 million Horizon Europe collaborative research project seeking to develop an end-to-end digital twin-driven framework for optimized manufacturing and maintenance of next-generation composite aircraft structures.

According to the company, “enhanced computational models will embed interdisciplinary knowledge of aircraft components and manufacturing/repair processes to support their optimization as well as enable data management and monitoring across the entire life cycle of the aircraft’s operation.”

GENEX will really ramp up in the New Year. It is projected to run through 2026.

According to a company press release about the initiative, “The core of the GENEX project is based on three [digital twin] blocks focused on different facets of the aircraft use life, which, eventually, will be integrated into a fourth to form the multidisciplinary digital twin.”

Each block is as follows:

     • Block 1 – Manufacturing Process Digital Twin: An automated tape laying (ATL) process, coupled with hybrid-twin simulation methods, will be developed for the eco-efficient and advance manufacture of recyclable thermoplastic composites.

      • Block 2 – Product Usage Digital Twin: Data- and physics-based machine learning (ML) algorithms for damage detection and location, combined with high-performance computing (HPC)-based multi-physics and AI-powered digital twin tools for fatigue life prediction.

      • Block 3 – MRO Digital Twin: Augmented reality tools, together with novel laser-assisted methods for surface cleaning and monitoring, smart monitoring, and in-situ tailored heating of composite repair blankets, will be further developed to provide additional assistance in manual scarf repair operations, increasing the reliability of the repair process, while supporting the modification and virtual certification of MRO practices.

    • Block 4 – Cognitive Digital Twin: Combined integration of blocks 1-3 for the realization of a digital twin-drive framework implemented into a common industrial internet of things (IIoT) platform.

“The aviation industry is facing a two-fold challenge — targeting carbon neutrality while also adopting the digitalization of next-generation aircraft,” Dr. Calvo-Echenique says. “In GENEX, we hope to provide the needed technological impulse to optimize composite components manufacturing, and operation and repair processes using digital twin strategies.”

Other Industries Benefiting from Digital Twin Technologies

As you can see from the GENEX project, AI and digital twinning are revolutionizing many industries, chief among them transportation. Cognitive digital twin technologies are proving invaluable for the predictive maintenance of high-value military vehicles, airplanes, ships, and even passenger cars. Digital twin solutions like those developed by CarTwin extend the lifespan of cars and other vehicles by monitoring the vehicle’s “health” through its “digital twin.”

Basically, CarTwin can provide diagnostic and predictive models for all vehicle systems for which data is available (either directly or indirectly) onboard the vehicle.
Virtually any part of the vehicle that has sensors or that sensors can be developed for can be “twinned.” These data sets are then enhanced and augmented with design and manufacturing data that is already available by the OEM.

Primarily designed for use in fleets of vehicles, in combination with powerful AI models, CarTwin predicts breakdowns, monitors and improves performance, measures and records real-time greenhouse gas emissions, which reduces expensive maintenance costs and avoids lost revenue associated with fleet downtime.

Rohit Mahajan is a Managing Partner at BigRio and the President and Co-Founder of Citadel Discovery. He has a particular expertise in the development and design of innovative AI and machine learning solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

CarTwin has leveraged AI and Digital Twin technologies to create a digital, cloud-based clone of a physical vehicle designed to detect, prevent, predict, and optimize through AI and real-time analytics. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

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What is Quantum Biology, and How Is it Changing Medicine for the Better?

AI and “quantum biology” may hold the key to changing medicine forever, maybe even the eradication of all disease!

Imagine a world where cancer, Alzheimer’s disease, Parkinson’s even aging itself have been defeated or can be treated in such a way that everyone can enjoy a long life of strength, vitality, and good health. That is the ultimate promise of artificial intelligence in healthcare, and it is AI combined with quantum computing which is creating the emerging field of “quantum biology” that could make that world a reality.

At its most basic meaning, the term “quantum” refers to something that is so small that it borders on the infinitesimal. In quantum physics, that means the smallest particle possible that still maintains the properties of the matter it is part of. In quantum biology, it may reveal the deepest level of understanding of how the body works and the real impact of disease that has thus far eluded even the most advanced medical science — and quantum computing will help.

Without getting into heavy details on how — quantum computing has processing power and memory capacity that is many orders of magnitude higher than conventional computers. With its ability to process massive amounts of data quickly, quantum computing, when combined with AI algorithms, may provide researchers with the in-depth information they need to unlock the innermost secrets of the human body.
One area where this is, albeit in its infancy, but already occurring is in Pharma research and drug discovery.

With quantum technology, the hope is that drug-trial simulations can eliminate the high development costs, reducing the barriers that prevent pharmaceutical companies from investing in developing treatments for rare diseases, speeding up trials, and improving patient outcomes.

This is especially true for some of the rarest yet most devastating conditions. It is an unfortunate economic reality that innovative treatments for rare diseases, no matter how fatal or debilitating they can be – rarely get developed because it is just too costly, relative to the people that would benefit and therefore pay for any drugs that were developed. But, with AI and quantum computing significantly reducing the costs of drug discovery at every phase – that profit motive can be removed. The development of effective treatment for all diseases, no matter how rare, could be possible, which could radically change healthcare for the better.

AI, Quantum Computing, and “The Virtual Patient”

Besides drug discovery, the other area in healthcare where AI combined with quantum computing is likely to make the biggest paradigm shift is in cognitive digital twin technologies or CDT. In CDT, a “digital twin” of a real-world system is created using AI algorithms. Digital twins are already being used to monitor the “healthspan” of high-value mechanical systems such as cars, boats, and airplanes. NOAA recently announced it is using AI to develop a “digital twin” of the Earth itself to track global climate change.
CDT is already being used in healthcare through the creation of “virtual organs,” which are being used in drug trials. But the Holy Grail of personalized healthcare is the creation of a digital twin of yourself. Just as digital twins of helicopters and fighter jets are used to monitor systems breakdowns for preventive maintenance – once each of us has our own cognitive digital twins, our doctors can monitor us for health issues before they become major concerns.

A “digital patient” will also make very specific and targeted therapeutics a reality, as your doctor can try different drugs on you virtually to see what is the most effective treatment without any fear of side effects or complications. Quantum computing will make the digital twinning of something as complex as the human body possible.
Currently, the pharmaceutical industry, and indeed all of healthcare by its very nature, has to take a one-size-fits-all approach. Every patient with a condition is treated with the same few drugs or treatment options. But know not all drugs are not effective for everyone. Quantum tech will allow doctors to personalize medicines to create uniquely tailored treatments for their patients, which will be a fundamental shift in healthcare.

How Big Rio Can Help

Quantum computing is still very much an emerging technology with large-scale and practical applications still a way off. However, the technology is steadily graduating from the lab and heading for the marketplace. In 2019, Google announced that it had achieved “quantum supremacy,” IBM has committed to doubling the power of its quantum computers every year, and numerous other companies and academic institutions are investing billions toward making quantum computing a commercial reality.

Quantum computing will take artificial intelligence and machine learning to the next level. The marriage between the two is an area to pay very close attention to for startups as well as for where Big Tech will be going over the next five to ten years, and where ever this road can take use BigRio will be there to help get startups and society as a whole to its ultimate destination.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

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Artificial Intelligence and the Early Detection of Osteoarthritis

Most patients and every medical practitioner know that when it comes to chronic debilitating diseases, the earlier they can be detected and treated, the better. This is one of the major promises of AI in healthcare, improved diagnostics for earlier detection and better patient outcomes.

The latest such application comes as researchers are developing an AI solution that can find the early sign of osteoarthritis of the knee. Like many AI-driven diagnostic enhancements, this one can see subtle signs on X-rays better than the human eye. This is critical because x-rays are the primary diagnostic method for early knee osteoarthritis. An early diagnosis can save the patient from unnecessary examinations, treatments, and even knee replacement surgery.

Osteoarthritis is the most common joint-related ailment globally. In Finland alone – where this research took place — it causes as many as 600,000 medical visits every year. It has been estimated to cost the national economy up to EUR1 billion every year.

The new AI-based method was trained to detect a radiological feature predictive of osteoarthritis from x-rays. The method was developed in cooperation with the Digital Health Intelligence Lab at the University of Jyvaskyla as a part of the AI Hub Central Finland project. It utilizes neural network technologies that are widely used globally.

“The aim of the project was to train the AI to recognize an early feature of osteoarthritis from an x-ray. Something that many experienced doctors can visually distinguish from the image, but cannot be done automatically, and is often missed by the untrained eye,” explains Anri Patron, the researcher responsible for the development of the method.

The anomaly the AI has been trained to automatically detect is to see if there is “spiking” on the tibial tubercles in the knee joint or not. Tibial spiking is known to be an early sign of osteoarthritis.

The researchers say that the AI matched human doctors’ assessment of the presence of spiking in nearly 90% of cases, instantly, without the need to scrutinize and deeply examine the x-rays as the human orthopedic surgeons did.

The research offers definitive proof that AI can support early diagnosis of osteoarthritis at the point of primary healthcare before a patient is referred to an orthopedic specialist, which can make a major difference in catching and treating knee arthritis early.

“If we can make the diagnosis in the early stages, we can avoid uncertainty and expensive examinations such as MRI scanning. In addition, the patient can be motivated to take measures to slow down or even stop the progression of symptomatic osteoarthritis. In the best possible scenario, the patient might even avoid joint replacement surgery,” sums up professor of surgery Juha Paloneva, one of the Finnish researchers on the project.

How BigRio Helps Healthcare AI Startups

Like the technology developed by the researchers with the Central Finland Health Care District, BigRio is also a facilitator and incubator for AI startups, particularly in healthcare. In fact, it was my father’s own battle with and eventual death from lung disease that set me on my path to finding ways to use AI to improve healthcare delivery.
Eventually, among our other success stories, we did collaborate with a researcher who is in the process of developing a cognitive digital twin of the human lung. Right now, that technology is being used specifically in the realm of testing inhalers for asthma patients, but like the UWS tool, it has broader implications for better diagnostics and treatments for COPD and other lung diseases.
We like to think of ourselves as a “Shark Tank for AI.”

If you are familiar with the TV series, then you know that, basically, what they do is hyper-accelerate the most important part of the incubation process – visibility. You can’t get better visibility than getting in front of celebrity investors and a TV audience of millions of viewers. Many entrepreneurs who have appeared on that program – even those who did not get picked up by the sharks – succeeded because others who were interested in their concepts saw them on the show.

At BigRio, we may not have a TV audience, but we can do the same. We have the contacts and the expertise to not only weed out the companies that are not ready, as the sharks on the TV show do but also mentor and get those that we feel are readily noticed by the right people in the biomedical community.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

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Six Impressive AI Startups Are All Based in Austria!

Some of the most innovative advances in AI are not taking place in the US; there are some powerful incubators all through Europe and places such as Israel. In fact, six of the most impressive new AI startups are all based in Austria!

Those that follow the IT industry have long pointed out that Austria has been the up-and-coming “Silicon Valley” of Europe, attracting very high-caliber talent and enabling some impressive AI startups. Here are six quite noteworthy AI startups that are located in Austria.

1. Adverity
Adverity, founded in 2015, assesses and visualizes expenses, performance, and returns. The program integrates campaign data from hundreds of data sources, including LinkedIn, Google Ads, and Facebook, before sending the findings to business management systems via native connectors. According to Marktechpost, the California-based AI news hub, the platform is used by well-known companies like Red Bull, IKEA, and Zurich Insurance and is accessible to agencies, brands, and e-commerce providers.

2. Robart
Also established in Austria, Robart provides full AI navigation solutions for mobile robots, including hardware, software, and connected IoT applications such as your robotic vacuum and similar devices. Using Robart’s solutions, robots can map a whole home, categorize it into rooms, design particular cleaning procedures, identify deviations like an open window, learn user patterns, and adjust to changes in user habits.

3. Cortical.io
Cortical.io provides free access to rudimentary tools like term disambiguation, text comparison, and keyword extraction on its website. The developers claim that such tools can reduce the time-consuming task of reviewing complex documents such as legal contracts by as much as 80%.

4. Medicus AI
Medicus AI created a medical app that explains and analyzes blood tests and medical reports and gives customers individualized healthcare recommendations in line with its results. Like Adverity the company was also founded in Austria in 2015. Medicus AI supports hospitals, diagnostic labs, and other healthcare facilities by improving interactions with patients by providing simple-to-understand health reports, guiding patients through disease treatment and prevention, as well as providing remote monitoring of patient behavior to ensure compliance and enhance long-term health outcomes.

5. Semanticlabs
Austria-based Semanticlabs creates tools for large-scale data analyses, such as semantic algorithms. The company has developed out-of-the-box solutions for collaborative document management, automated tagging, and topic extraction from text using techniques for natural language processing. According to Marktechpost, their customers include Kronen Zeitung, the largest newspaper in Austria, and Erste Group, one of the largest financial service providers in Central and Eastern Europe.

6. Scarlet red
Scarletred is another AI healthcare solution designed to improve diagnostics, in this case, for the remote detection of various skin disorders. The company was founded in 2014. The platform comprises a web tool for picture processing, an iOS app, and a skin patch that calibrates photographs for different light and distance situations. Patients upload a photo of the region being examined and their skin tag to their healthcare provider’s online portal using the iOS app. The automated skin area analysis is then performed by the web-based analytical platform using computer vision.

How BigRio Helps Facilitate Investment in AI Startups
Like the agencies and investors who are helping places like Austria become hubs of AI innovation, BigRio is also a powerful facilitator and incubator for AI startups in the US and around the world. We specialize in bringing healthcare AI solutions such as Medicus AI and Scarletred mentioned above to market.

We like to think of ourselves as a “Shark Tank for AI.”

If you are familiar with the TV series, then you know that, basically, what they do is hyper-accelerate the most important part of the incubation process – visibility. You can’t get better visibility than getting in front of celebrity investors and a TV audience of millions of viewers. Many entrepreneurs who have appeared on that program – even those who did not get picked up by the sharks – succeeded because others who were interested in their concepts saw them on the show.

At BigRio, we may not have a TV audience, but we can do the same. We have the contacts and the expertise to not only weed out the companies that are not ready, as the sharks on the TV show do but also mentor and get those that we feel are readily noticed by the right people who have a vested interest in advancing AI and Machine learning technologies.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

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Demand for Smart Cars and Smart Cities Boosts AI Chip Market!

According to a new report, major consumer demand for smart cars, smart homes, and other buildings in “smart cities” is driving significant expansion in the AI chip market.

“Smart cities” are the latest growth area in AI. A “smart city” refers to a city that uses digital or information-communication technology to improve the comfort and efficiency of human life. One strategy for urbanization that aims to achieve sustainable growth is making cities equipped with cutting-edge features for residents to live, walk, shop, and enjoy a safer and more convenient existence – much of which is now being driven by AI.

For instance, in March 2022, Saudi Arabia launched a new smart city project in Jeddah for light industries and auto repair, opening the first two stages of the city, the second of which is a 350-square-mile labor city. A total of three thousand square miles is covered by the Smart City initiative. Further, According to Saudi Vision 2030, a Saudi Arabia-based strategic framework, the city is distinguished by an interconnected infrastructure and the application of digital and smart technology to offer automated services to customers.

According to The Business Research Company’s Artificial Intelligence Chip Global Market Report 2022, the increase in demand for smart homes and smart cities is driving significant artificial intelligence chip market growth. Per the report, the global artificial intelligence chip market size is expected to grow from $10.55 billion in 2021 to $15.01 billion in 2022 at a compound annual growth rate (CAGR) of 42.3%.

The press release went on to say that technological advancement is a key trend gaining popularity in the artificial intelligence chip market. Major companies in the artificial intelligence chip market, like NVIDIA, are advancing in their new technologies and focusing much of their R&D specifically on AI chips, such as the development of NVIDIA A100 chips which have been designed to streamline AI training and inference and improve efficiency.

The report also said that the major players in the artificial intelligence chip market are Intel Corporation, Mediatek Inc, NVIDIA Corporation, Qualcomm Technologies Inc, Advanced Micro Devices Inc, Alphabet Inc, NXP Semiconductors NV, Micron Technology Inc, IBM Corporation, Apple Inc, Huawei Technologies Co. Ltd, Mythic Inc, Samsung Electronics Co. Ltd, LG Corporation, and Google LLC.

North America was the largest region in the artificial intelligence market in 2021. The regions covered in this artificial intelligence chip market research are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, the Middle East, and Africa.

The report also gives an in-depth analysis of the impact of COVID-19 on the market. The reports draw on 1,500,000 datasets, extensive secondary research, and exclusive insights from interviews with industry leaders.

AI and CarTwin

In addition to increasing demand for smart chips for fully autonomous vehicles, the other major impact that AI is having on the automotive and transportation industry is in the use of cognitive digital twin technologies for predictive maintenance. Digital twin solutions like those developed by CarTwin extend the lifespan of cars and other vehicles by monitoring the vehicle’s “health” through its “digital twin.”

Basically, CarTwin can provide diagnostic and predictive models for all vehicle systems for which data is available (either directly or indirectly) onboard the vehicle.

Virtually any part of the vehicle that has sensors or that sensors can be developed for can be “twinned.” These data sets are then enhanced and augmented with design and manufacturing data that is already available by the OEM.

Primarily designed for use in fleets of vehicles, in combination with powerful AI models, CarTwin predicts breakdowns, monitors and improves performance, and measures and records real-time greenhouse gas emissions, which reduces expensive maintenance costs and avoids lost revenue associated with fleet downtime.

It is very likely in the not-too-distant future, as fully autonomous vehicles replace human-driven fleets of over-road cargo transportation and taxis and limousine services, that technology such as CarTwin’s will also be incorporated into the algorithms to keep these vehicles not only self-driving but safe and on the road longer.

Rohit Mahajan; is a Managing Partner at BigRio and the President and Co-Founder of Citadel Discovery. He has a particular expertise in the development and design of innovative AI and machine learning solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

CarTwin has leveraged AI and Digital Twin technologies to create a digital, cloud-based clone of a physical vehicle designed to detect, prevent, predict, and optimize through AI and real-time analytics. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

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