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.

 

/by

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.

/by

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.

/by

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.

/by

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.

/by

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.

/by

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.

/by

AI Is Being Used for Earlier Detection of Urinary Tract Infections

Urinary tract infections, commonly known as UTIs, usually do not pose serious health risks – when they are detected and treated early. However, when allowed to advance undetected past a certain point, a number of serious adverse outcomes can result from late or misdiagnosis of UTI.

A group of researchers from the University of Edinburgh and Heriot-Watt University are developing artificial intelligence and “socially assistive robots” to detect urinary tract UTIs earlier and ensure better patient outcomes.

UTIs affect 150 million people worldwide annually, making it one of the most common types of infection. When diagnosed early, it can be treated with antibiotics. If left untreated, UTIs can lead to sepsis, kidney damage, and even loss of life.

Diagnosis, however, can be difficult with lab analysis, a process taking up to 48 hours, providing the only definitive result. Early signs of a UTI can also be challenging to recognize because symptoms vary according to age and existing health conditions. There is no single sign of infection but a collection of symptoms which may include pain, fever, increased need to urinate, changes in sleep patterns, and tremors.

To address these concerns, the researchers are working with two industry partners from the care sector who are helping the scientists to develop machine learning methods and interactions with socially assistive robots to support earlier detection of potential infections and raise an alert for investigation by a clinician.

The project will gather continual data about the daily activities of individuals in their homes via sensors that could help spot changes in behavior or activity levels and trigger an interaction with a socially assistive robot. Known as “FEATHER,” the AI platform will combine and analyze these data points to flag potential infection signs before an individual or caretaker is even aware that there is a problem. Behavioral changes that could indicate UTI include changes in walking pace, increased frequency of urination, changes in cognitive function, or a change in sleep patterns, all of which could be noticed and documented by interaction with the assistive robot.

The AI and implementation aspects of the project will be led by Professor Kia Nazarpour, Dr. Nigel Goddard, and Dr. Lynda Webb from the University of Edinburgh. The Human-Robot Interaction aspects will be led by Professor Lynne Baillie, assisted by Dr. Mauro Dragone, from Heriot-Watt University.

Professor Kia Nazarpour, project lead and Professor of Digital Health at the School of Informatics, University of Edinburgh, said, “This unique data platform will help individuals, caretakers, and clinicians to recognize the signs of potential urinary tract infections far earlier, helping to prompt the investigations and medical tests needed. Earlier detection makes timely treatment possible, improving outcomes for patients, lowering the number of people presenting at hospital, and reducing costs to the NHS.”

How BigRio Helps Bring Advanced AI Solutions to Healthcare

Like the FEATHER project, improving disease detection, medical imaging, and diagnostics is an area where AI and machine learning are making one of the technology’s biggest impacts.

BigRio prides itself on being a facilitator and incubator for such advances in leveraging AI to improve diagnostics. 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 outcomes.

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 FEATHER UTI detection 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.

/by

Digital Twins Are Reinventing Clinical Drug Trials

A cognitive digital twin is an AI-driven representation of a real-world system. The objects being twinned can be mechanical such as vehicles, ships, and airplanes, or biological, such as organs and biological processes. It is the latter that is radically altering pharmaceutical research and may very well change the nature of clinical drug trials forever.

Traditional drug discovery is a long and complex process that can take years and millions and millions of dollars. The long and intensive process of bringing a new drug through all phases of clinical trials and to market starts with recruiting the right candidates and then proceeds through many steps and phases of testing the drugs vs. placebos in the candidates that have been recruited for the trial.

Finding those patients is one of the most time-consuming aspects of the process. But that is all changing thanks to AI and, specifically, cognitive digital twin (CDT) technologies.

Cognitive digital twins behave virtually the same way, statistically, as their physical counterparts, which makes them ideal for the powerful ability of AI to assimilate massive amounts of data and make remarkably accurate predictions.

Digital twins have been used quite effectively for monitoring health and providing preventive maintenance for some very highly complex systems, such as high-performance sports cars to military aircraft.

Now, they are changing the very landscape of drug discovery by modeling perhaps the most complex system of all organs and even complete human beings. For example, digital twins of patients are now being used to find ideal candidates in that all-important recruitment phase of a drug trial. The twin is created using AI algorithms and machine learning to create a “virtual patient” by leveraging data from previous clinical trials and from individual patient records. The model predicts how the patient’s health would progress during the course of the trial.

This kind of CDT technology is also being used to create “virtual patients” who are “stand-ins” for the control group – the ones getting a placebo – in the typical double-blind drug trial protocol. The digital twin patient predicts how that individual patient would react if they were given a placebo, essentially creating a simulated control group for a particular patient. Think of it as splitting yourself into two distinct exact copies of yourself, one given the actual drug and the other given the placebo as a control. This makes for an even more accurate control group than just splitting all those in the trial into two groups as in typical trials, because the control group is now exactly the same as the group getting the drug. The digital twin virtually eliminates any variance between the drug group and the placebo group that could be based on genetic, physical, and lifestyle differences between the two groups.

Furthermore, replacing or augmenting control groups with digital twins could help patient volunteers as well as researchers. Most people who join a trial do so, hoping to get a new drug that might help them when already-approved drugs have failed. But there’s a 50/50 chance they’ll be put into the control group and won’t get the experimental treatment. Replacing control groups with digital twins could mean more people have access to experimental drugs.

In Silico Research

And finally, another area where CDT technology is making a tremendous difference in drug discovery is in the emerging area of “in silico” research, where digital twinning is used to create so-called “organs on a chip.” Digital twins of the human heart, lungs, and other organs are already being used to hyper-accelerate drug discovery.

One of the promises of CDT is to make complete in silico drug trials from start to finish a reality. Early successes occurring now are paving the way to a time in the not-so-distant future where no humans, nor animals, not even a single living cell will be required for drug discovery — and yet the impact of any given therapeutic or treatment option on a targeted organ, system or even an individual cell can be perfectly charted.

Citadel and AI for Drug Discovery

AI and machine learning are having a tremendous impact on healthcare in America, from streamlining hospital operations, to improved diagnostics and more intuitive telemedicine applications. However, AI’s greatest impact will likely be in the way digital twins and other AI solutions are revolutionizing pharmaceutical research.

To that end, 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.

The costs of drug discovery continue to rise, with current estimates exceeding $2 Billion. Not to mention that bringing a drug successfully through all clinical trial phases takes, on average, 10-12 years in research and development. Artificial intelligence and machine learning in drug discovery hold the key to reducing these costs and timelines.

Rohit Mahajan is the President and Co-Founder of Citadel Discovery. 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.

Citadel Discovery is dedicated to leveraging AI and MI for the purpose of democratizing access to the data and technology that will drive the future of biological exploration, drug discovery, and health technologies. 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.

/by

NOAA is Creating a Digital Twin of the Planet Earth!

A digital twin is a digital representation of a physical object or system. Cognitive digital twin (CDT) technology uses AI to create highly sophisticated “twins” or models to accurately mimic a real-world object. That object could be a car, an airplane, ship, or helicopter. Increasingly in the healthcare field, CDT has been used to “twin” organs and biological systems for research, diagnostics, and health maintenance. Digital twins have even been built to represent and understand regions and cities.

Now, in perhaps one of the most complex and ambitious uses of CDT to date, the National Oceanographic and Atmospheric Association (NOAA) has announced its plans to create a digital twin of the planet Earth to track global warming and other environmental issues!

The agency has partnered with NVIDIA and Lockheed Martin to construct the Earth Observation Digital Twin, an inaugural prototype of Earth modeled on real-time geophysical data sourced from satellites and ground stations.

According to NOAA, the replica Earth, or EODT, will be designed as a two-dimensional computer program. Some potential climate impacts the EODT can display include global glacier melting, drought impacts, wildfire prediction, and other climate change events.

“We’re providing a one-stop shop for researchers, and for next-generation systems, not only for current, but for recent past environmental data,” Lockheed Martin Space Senior Research Scientist Lynn Montgomery said. “Our collaboration with NVIDIA will provide NOAA a timely, global visualization of their massive datasets.”

Emerging technologies like artificial intelligence play a key role in EODT’s data processing and modeling. Matt Ross, a senior manager at Lockheed Martin, said that the sheer volume and diversity of data from NOAA sources that program EODT make it challenging to gauge accurate insights from the application without the use of AI.

“This data happens to come in different formats, because the data are so diverse, because it’s measuring so much different stuff,” Ross said. “It arrives in different formats that, absent technology, it could make it very, very difficult to gain the insights that NOAA needs to make decisions.”

Leveraging the power of AI and machine learning algorithms will help NOAA researchers assimilate and identify the incoming data, as well as detect any anomalies. Ross added that the combined power of AI and ML data processing is key to Lockheed and NVIDIA’s “digital twin” programming technology in that it can accurately model past data as well as future realities, all in an intractable and real-time interface.

While both NVIDIA and Lockheed intend for the final deliverable to be a two-dimensional user experience, additional capabilities may be added in the future.

“The fact that we can pull all this data into a single sort of format, in a single viewpoint, allows you to have real-time or near real time, access to it, and the interdependencies of that data to make real-time decisions,” said Dion Harris, the lead product manager of accelerated computing at NVIDIA.

Other Industries Benefiting from Digital Twin Technologies

In addition to paving the way for a digital twin of the planet itself to monitor climate change, AI and digital twinning are revolutionizing many other industries, chief among them transportation. Just as CDT can monitor the health of the Earth, cognitive digital twin technologies are proving invaluable for 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.

/by