BRN Discussion Ongoing

[Frank Zappa]

How Nvidia, TSMC, Broadcom and Qualcomm will lead a trillion-dollar silicon boom - SiliconANGLE

How Nvidia, TSMC, Broadcom and Qualcomm will lead a trillion-dollar silicon boom - SiliconANGLE

siliconangle.com

Nice post. Thankyou

 

[Fullmoonfever]

Nice to see Tata Elxsi still on the neuromorphic train.

Multimodal AI and Neuromorphic AI: Detection, Diagnosis, Prognosis - Tata Elxsi

Discover how Multimodal and Neuromorphic AI lead healthcare from reactive to proactive. This article delves into Responsible AI and its transformative impact.

ai.tataelxsi.com

MULTIMODAL AI AND NEUROMORPHIC AI: DETECTION, DIAGNOSIS, PROGNOSIS

Navigate​

The synergy of cutting-edge technologies like Multimodal and Neuromorphic AI signals a pivotal shift from reactive to proactive healthcare. This article explores captivating use cases, offering insights on the implementation of Responsible AI.
Join us as we navigate the frontier of healthcare, where the synergy of innovation and responsibility promises a revolution in patient care and well-being.

Current state of AI adoption in Healthcare​

Unlocking the full potential of AI in healthcare is an uncharted journey. From optimising drug combinations to spearheading clinical correlation and toxicity studies, AI is set to redefine every facet of the industry. Despite its transformative capabilities, AI remains a niche technology, requiring a nuanced understanding of its application in healthcare.

The tides are changing as the healthcare sector recognises the urgency for an interdisciplinary approach, marrying engineering with medical science. This paradigm shift signals an imminent era where AI’s vast capabilities will revolutionise diagnostics, patient treatment, protocols, drug development and delivery, and prescription practices over the next decade.

Join us as we navigate the frontier of healthcare, where the synergy of innovation and responsibility promises a revolution in patient care and well-being.

Multimodal AI and Neuromorphic Technology – A new era in Preventive Healthcare​

In the ever-evolving landscape of healthcare, the amalgamation of Multimodal AI and Neuromorphic Technology marks a pivotal moment—a shift from reactive medicine to a proactive, preventive healthcare paradigm. This synergy is not just a collaboration of cutting-edge technologies; it’s a gateway to a future where wellness takes centre stage.

These technologies hold promise to transform healthcare by enhancing diagnostics, enabling personalised medicine, predicting long-term prognosis and contributing to innovations in therapeutic interventions.

Let’s delve into compelling use cases and glimpse the future of preventive healthcare.

The tides are changing as the healthcare sector recognises the urgency for an interdisciplinary approach, marrying engineering with medical science. This paradigm shift signals an imminent era where AI’s vast capabilities will revolutionise diagnostics, patient treatment, protocols, drug development and delivery, and prescription practices over the next decade.

Join us as we navigate the frontier of healthcare, where the synergy of innovation and responsibility promises a revolution in patient care and well-being.

Defining Multimodal and Neuromorphic AI​

Multi-modal AI​

Multimodal AI refers to the artificial intelligence systems that process and analyse data from multiple modalities or sources. In healthcare, these modalities often include both visual and clinical data. Visual data may include medical images from scans, while clinical data encompasses patient records, parameters, and test reports. Multimodal AI integrates these diverse data types to provide a comprehensive understanding, draw meaningful insights and give suggestions based on data and image analytics.

Neuromorphic Technology​

The term “neuromorphic” comes from the combination of “neuro” (related to the nervous system) and “morphic” (related to form or structure). Neuromorphic technology is an innovative approach in computing that draws inspiration from the structure and function of a human brain. These are AI powered by brain-like computing architectures. It can help process larger amount of data with less computing power, memory and electric power consumption. Neuromorphic Technology utilises Artificial Neural Networks (ANN) and Spiking Neural Networks (SNN) to mimic the parallel processing, event-driven nature, and adaptability observed in biological brains.

Multimodal Inputs​

• Medical Images
• Lab Reports
• Clinical History
• Patient Demographic Information

Fusion Module​

Cross-modal attention mechanism to dynamically weigh the importance of text, video, and other parameters for calculating index to decide priority of selection.

Inference Outputs​

Inference Results for

• Diagnostic
• Prognostic
• Lifestyle Recommendation
• Disease Prediction

Use Cases of Multimodal and Neuromorphic AI​

Early Screening & Disease Detection​

Multimodal AI​

• Integrates visual and clinical data for holistic analysis.
• Advanced –image recognition for early detection.
• Comprehensive patient profiling.

Neuromorphic Technology​

• Efficient pattern recognition for subtle disease indicators.
• Event-driven processing for real-time detection. This is crucial for detecting anomalies or irregularities that may be early signs of diseases.
• Continuous monitoring for dynamic changes. This continuous surveillance is especially valuable for conditions with varying symptoms.

Diagnosis​

Multimodal AI​

• Integrated diagnostic insights from diverse data.
• Cross-verification for reliability.
• Tailored treatment plans based on nuanced understanding.
• Continuous updates based on latest findings reported in the subject.

Neuromorphic Technology​

• Large and Efficient data processing with minimal energy consumption. This efficiency contributes to faster and more accurate diagnoses.
• Allows integration of more complex algorithms on wearable devices; makes diagnostics more real-time and helps timely interventions
• Implantable devices can be made AI enabled with Neuromorphic computing, due to the low computing requirement and power consumption, making the diagnosis and management more precise and real-time.
• Adaptive intelligence for dynamic adjustments. This adaptability enhances the precision of diagnostic processes. This event-driven processing aligns with the dynamic nature of healthcare data allowing for more accurate and timely diagnoses.
• SNN for real-time response
  and accuracy.

Prognosis​

Multimodal AI​

• Research Advancements: Facilitates discovery of new insights, contributing to medical advancements and innovations.
• Personalised Prognostic Models: Considering both visual and clinical data, these models account for individual variations, and correlate with prior case records and provide more accurate predictions of disease outcomes.
• Dynamic Adaptability: The adaptability of multimodal AI to changing data patterns ensures that prognostic models can dynamically adjust based on evolving patient conditions and improve prognosis predictions

Neuromorphic Technology​

• Analysis of longitudinal data for predicting disease progression.
• Dynamic adaptability in prognostic models that can adjust to changing data patterns. This adaptability improves prognosis prediction accuracy for evolving patient conditions.
• Personalised prognostic insights based on individual variations can help in more accurate predictions tailored to individual patient profiles.

Tata Elxsi Use Case​

Disease Detection and Diagnosis​

Utilising Neuromorphic Technology, we’ve achieved significant advancements in the analysis of medical images on low-computing embedded platforms, enabling on-field diagnostics of ultrasound images.

This innovative approach provides critical diagnostic information for musculoskeletal injuries, including tissue damage extent, recovery progress, and healing time predictions, all with enhanced efficiency and device portability making it ideal for applications such as sports medicine.

Applications of Multimodal and Neuromorphic AI​

Multimodal AI​

• Comprehensive Patient Analysis
• Diagnostic Accuracy
• Mental Health and Behavioural Analysis
• Lifestyle Reviews and Recommendations
• Management of Chronic Diseases like Diabetes/HT/Cardiac Diseases with continous monitoring and personalised medications
• Diagnosis/Management and Prognosis of various types of cancers, Digital Drug Trials, Effective Pandemic Surveillance and staging, Gene Therapy and Genomics
• Recommendations for interventions and Prioritisation of therapeutic resources and modalities

Neuromorphic Technology​

• Implants, Wearables Devices
• Processing Large Data
• Medical Imaging Analysis
• Drug Discovery and Personalised Medicine
• Robotic Surgery Assistance
• Neurological Disorder Understanding
• Patient Care and Rehabilitation
• Predictive Analytics for Healthcare Management
• Energy Efficient Remote Monitoring

The Synergy of MLOps and Advanced AI​

The transformative impact of MLOps across operational efficiency, data management, patient outcomes, and the overall quality of care is unmistakable. In the quest for advancing healthcare, the convergence of Machine Learning Operations (MLOps) with Multimodal and Neuromorphic AI has emerged as a game-changer. These technologies can help in seamless deployment, continuous monitoring, and collaborative development across various stakeholders in the healthcare ecosystem.

While Advanced AI Technologies offer the potential for improvising the use cases, the application of MLOps can be instrumental in strengthening and regulating these advancements. It achieves this by bringing in streamlined AI development processes, dataset management, continuous monitoring of model accuracy across different versions, ensuring the deployment of thoroughly vetted versions for clinical use.

Additonally, MLOps frameworks enable and learn from deviations, further enhancing their efficacy in healthcare applications.

Use Cases​

Disease Detection​

Disease Prediction and Prevention
Real World Application – Early Detection of Chronic Disease, Infectious Disease Monitoring
Healthcare Fraud Detection
Real World Application – Claims Analysis, Identify Theft Prevention
Medical Imaging Analysis
Real World Application – Early Cancer Detection, Neurological Disorders Diagnosis
Genomic Research
Real World Application – Cancer Genomics, Rare Genetic Diseases

Diagnosis​

Drug Discovery and Development
Real World Application – Protein Folding Prediction, Drug Toxicity Prediction
Personalised Medicine
Real World Application – Oncology and Targeted Therapies, Chronic Disease Management
Healthcare Resource Management
Real World Application – Emergency Room Digitalised Management, Pharmaceutical Supply Chain Management

Prognosis​

Prediction of Clinical Outcome
Real World Application – Prediction of recovery time and quality of life, Adverse Effects, Short term and long term impact
Remote Patient Monitoring
Real World Application – Chronic Disease Management, Post Surgery Monitoring

Responsible AI – Navigating Ethical Frontiers​

In the realm of AI, addressing bias stands as a pivotal ethical imperative, particularly in fields like medical analysis where the demand for precision is ethically, legally, and morally paramount. As AI practitioners, our commitment to responsible AI requires rigorous testing using diverse, unbiased anonymised datasets, continual monitoring to mitigate biases, and a steadfast dedication to achieving fair outcomes across diverse patient populations.

Moreover, the ethical considerations extend to the strategic utilisation of data. The foundation of responsible AI in healthcare is laid upon a robust ethical framework that guides the entire lifecycle of Neuromorphic and Multimodal AI systems. Stakeholders must unwaveringly adhere to established ethical principles, ensuring transparency, fairness, and accountability at every stage of AI implementation.

When we delve into the realm of Gen AI, the potential for malpractice looms. Consider scenarios where a patient, with normal renal function manageable through medication, undergoes a renal scan. Unscrupulous use of Gen AI could manipulate images, creating false lesions, leading to unnecessary surgeries or even nephrectomy which can benefit illegal organ trade

Thus, the imperative lies in defining strong ethical boundaries, implementing robust audits, and establishing legal frameworks to prevent data manipulation and ensure the highest standards of integrity.

In embracing these technological advancements responsibly, we are not just witnessing the future of healthcare; we are actively shaping it. The era of proactive, preventive healthcare beckons, promising a future where wellness is at the forefront of the industry’s evolution.

The shift from traditional, one-size-fits-all medical practices, prone to misinterpretation and diagnostic errors, to AI-enhanced methodologies, heralds a new era of precision and personalised care. AI’s capability to analyse a broad spectrum of patient data—ranging from genetic backgrounds to lifestyle factors—promises a departure from misdiagnoses and introduces tailored therapeutic interventions.

AI and multimodal technologies enable a holistic view of the patient’s health, integrating diverse data points. While, Neuromorphic computing advances the portability of medical devices, including wearables and implants, transforming them into intelligent systems capable of adapting to varying conditions.

As thought leaders in the healthcare industry, our commitment to responsibly integrate these technologies paves the way for a future where healthcare is not only reactive, but anticipatory, personalised, and universally accessible.

Author

Anup S S
Practice Head, Artificial Intelligence, Tata Elxsi
Anup S.S. is a visionary in leveraging Artificial Intelligence, Machine Learning and Deep Learning. Leading breakthrough AI projects in healthcare, Anup’s strategic insight and innovation ignite client success, unlocking AI’s full potential.

Book Your Exclusive Print Copy Now!​

© 2024 TATA ELXSI
PRIVACY POLICY
COOKIE POLICY
TERMS OF USE
Facebook Twitter Youtube Linkedin Instagram Envelope

 

[SharesForBrekky]

Nice to see Tata Elxsi still on the neuromorphic train.

Multimodal AI and Neuromorphic AI: Detection, Diagnosis, Prognosis - Tata Elxsi

Discover how Multimodal and Neuromorphic AI lead healthcare from reactive to proactive. This article delves into Responsible AI and its transformative impact.

ai.tataelxsi.com

MULTIMODAL AI AND NEUROMORPHIC AI: DETECTION, DIAGNOSIS, PROGNOSIS

Navigate​

The synergy of cutting-edge technologies like Multimodal and Neuromorphic AI signals a pivotal shift from reactive to proactive healthcare. This article explores captivating use cases, offering insights on the implementation of Responsible AI.
Join us as we navigate the frontier of healthcare, where the synergy of innovation and responsibility promises a revolution in patient care and well-being.

Current state of AI adoption in Healthcare​

Unlocking the full potential of AI in healthcare is an uncharted journey. From optimising drug combinations to spearheading clinical correlation and toxicity studies, AI is set to redefine every facet of the industry. Despite its transformative capabilities, AI remains a niche technology, requiring a nuanced understanding of its application in healthcare.

The tides are changing as the healthcare sector recognises the urgency for an interdisciplinary approach, marrying engineering with medical science. This paradigm shift signals an imminent era where AI’s vast capabilities will revolutionise diagnostics, patient treatment, protocols, drug development and delivery, and prescription practices over the next decade.

Join us as we navigate the frontier of healthcare, where the synergy of innovation and responsibility promises a revolution in patient care and well-being.

Multimodal AI and Neuromorphic Technology – A new era in Preventive Healthcare​

In the ever-evolving landscape of healthcare, the amalgamation of Multimodal AI and Neuromorphic Technology marks a pivotal moment—a shift from reactive medicine to a proactive, preventive healthcare paradigm. This synergy is not just a collaboration of cutting-edge technologies; it’s a gateway to a future where wellness takes centre stage.

These technologies hold promise to transform healthcare by enhancing diagnostics, enabling personalised medicine, predicting long-term prognosis and contributing to innovations in therapeutic interventions.

Let’s delve into compelling use cases and glimpse the future of preventive healthcare.

The tides are changing as the healthcare sector recognises the urgency for an interdisciplinary approach, marrying engineering with medical science. This paradigm shift signals an imminent era where AI’s vast capabilities will revolutionise diagnostics, patient treatment, protocols, drug development and delivery, and prescription practices over the next decade.

Join us as we navigate the frontier of healthcare, where the synergy of innovation and responsibility promises a revolution in patient care and well-being.

Defining Multimodal and Neuromorphic AI​

Multi-modal AI​

Multimodal AI refers to the artificial intelligence systems that process and analyse data from multiple modalities or sources. In healthcare, these modalities often include both visual and clinical data. Visual data may include medical images from scans, while clinical data encompasses patient records, parameters, and test reports. Multimodal AI integrates these diverse data types to provide a comprehensive understanding, draw meaningful insights and give suggestions based on data and image analytics.

Neuromorphic Technology​

The term “neuromorphic” comes from the combination of “neuro” (related to the nervous system) and “morphic” (related to form or structure). Neuromorphic technology is an innovative approach in computing that draws inspiration from the structure and function of a human brain. These are AI powered by brain-like computing architectures. It can help process larger amount of data with less computing power, memory and electric power consumption. Neuromorphic Technology utilises Artificial Neural Networks (ANN) and Spiking Neural Networks (SNN) to mimic the parallel processing, event-driven nature, and adaptability observed in biological brains.

Multimodal Inputs​

• Medical Images
• Lab Reports
• Clinical History
• Patient Demographic Information

Fusion Module​

Cross-modal attention mechanism to dynamically weigh the importance of text, video, and other parameters for calculating index to decide priority of selection.

Inference Outputs​

Inference Results for

• Diagnostic
• Prognostic
• Lifestyle Recommendation
• Disease Prediction

Use Cases of Multimodal and Neuromorphic AI​

Early Screening & Disease Detection​

Multimodal AI​

• Integrates visual and clinical data for holistic analysis.
• Advanced –image recognition for early detection.
• Comprehensive patient profiling.

Neuromorphic Technology​

• Efficient pattern recognition for subtle disease indicators.
• Event-driven processing for real-time detection. This is crucial for detecting anomalies or irregularities that may be early signs of diseases.
• Continuous monitoring for dynamic changes. This continuous surveillance is especially valuable for conditions with varying symptoms.

Diagnosis​

Multimodal AI​

• Integrated diagnostic insights from diverse data.
• Cross-verification for reliability.
• Tailored treatment plans based on nuanced understanding.
• Continuous updates based on latest findings reported in the subject.

Neuromorphic Technology​

• Large and Efficient data processing with minimal energy consumption. This efficiency contributes to faster and more accurate diagnoses.
• Allows integration of more complex algorithms on wearable devices; makes diagnostics more real-time and helps timely interventions
• Implantable devices can be made AI enabled with Neuromorphic computing, due to the low computing requirement and power consumption, making the diagnosis and management more precise and real-time.
• Adaptive intelligence for dynamic adjustments. This adaptability enhances the precision of diagnostic processes. This event-driven processing aligns with the dynamic nature of healthcare data allowing for more accurate and timely diagnoses.
• SNN for real-time response
  and accuracy.

Prognosis​

Multimodal AI​

• Research Advancements: Facilitates discovery of new insights, contributing to medical advancements and innovations.
• Personalised Prognostic Models: Considering both visual and clinical data, these models account for individual variations, and correlate with prior case records and provide more accurate predictions of disease outcomes.
• Dynamic Adaptability: The adaptability of multimodal AI to changing data patterns ensures that prognostic models can dynamically adjust based on evolving patient conditions and improve prognosis predictions

Neuromorphic Technology​

• Analysis of longitudinal data for predicting disease progression.
• Dynamic adaptability in prognostic models that can adjust to changing data patterns. This adaptability improves prognosis prediction accuracy for evolving patient conditions.
• Personalised prognostic insights based on individual variations can help in more accurate predictions tailored to individual patient profiles.

Tata Elxsi Use Case​

Disease Detection and Diagnosis​

Utilising Neuromorphic Technology, we’ve achieved significant advancements in the analysis of medical images on low-computing embedded platforms, enabling on-field diagnostics of ultrasound images.

This innovative approach provides critical diagnostic information for musculoskeletal injuries, including tissue damage extent, recovery progress, and healing time predictions, all with enhanced efficiency and device portability making it ideal for applications such as sports medicine.

Applications of Multimodal and Neuromorphic AI​

Multimodal AI​

• Comprehensive Patient Analysis
• Diagnostic Accuracy
• Mental Health and Behavioural Analysis
• Lifestyle Reviews and Recommendations
• Management of Chronic Diseases like Diabetes/HT/Cardiac Diseases with continous monitoring and personalised medications
• Diagnosis/Management and Prognosis of various types of cancers, Digital Drug Trials, Effective Pandemic Surveillance and staging, Gene Therapy and Genomics
• Recommendations for interventions and Prioritisation of therapeutic resources and modalities

Neuromorphic Technology​

• Implants, Wearables Devices
• Processing Large Data
• Medical Imaging Analysis
• Drug Discovery and Personalised Medicine
• Robotic Surgery Assistance
• Neurological Disorder Understanding
• Patient Care and Rehabilitation
• Predictive Analytics for Healthcare Management
• Energy Efficient Remote Monitoring

The Synergy of MLOps and Advanced AI​

The transformative impact of MLOps across operational efficiency, data management, patient outcomes, and the overall quality of care is unmistakable. In the quest for advancing healthcare, the convergence of Machine Learning Operations (MLOps) with Multimodal and Neuromorphic AI has emerged as a game-changer. These technologies can help in seamless deployment, continuous monitoring, and collaborative development across various stakeholders in the healthcare ecosystem.

While Advanced AI Technologies offer the potential for improvising the use cases, the application of MLOps can be instrumental in strengthening and regulating these advancements. It achieves this by bringing in streamlined AI development processes, dataset management, continuous monitoring of model accuracy across different versions, ensuring the deployment of thoroughly vetted versions for clinical use.

Additonally, MLOps frameworks enable and learn from deviations, further enhancing their efficacy in healthcare applications.

Use Cases​

Disease Detection​

Disease Prediction and Prevention
Real World Application – Early Detection of Chronic Disease, Infectious Disease Monitoring
Healthcare Fraud Detection
Real World Application – Claims Analysis, Identify Theft Prevention
Medical Imaging Analysis
Real World Application – Early Cancer Detection, Neurological Disorders Diagnosis
Genomic Research
Real World Application – Cancer Genomics, Rare Genetic Diseases

Diagnosis​

Drug Discovery and Development
Real World Application – Protein Folding Prediction, Drug Toxicity Prediction
Personalised Medicine
Real World Application – Oncology and Targeted Therapies, Chronic Disease Management
Healthcare Resource Management
Real World Application – Emergency Room Digitalised Management, Pharmaceutical Supply Chain Management

Prognosis​

Prediction of Clinical Outcome
Real World Application – Prediction of recovery time and quality of life, Adverse Effects, Short term and long term impact
Remote Patient Monitoring
Real World Application – Chronic Disease Management, Post Surgery Monitoring

Responsible AI – Navigating Ethical Frontiers​

In the realm of AI, addressing bias stands as a pivotal ethical imperative, particularly in fields like medical analysis where the demand for precision is ethically, legally, and morally paramount. As AI practitioners, our commitment to responsible AI requires rigorous testing using diverse, unbiased anonymised datasets, continual monitoring to mitigate biases, and a steadfast dedication to achieving fair outcomes across diverse patient populations.

Moreover, the ethical considerations extend to the strategic utilisation of data. The foundation of responsible AI in healthcare is laid upon a robust ethical framework that guides the entire lifecycle of Neuromorphic and Multimodal AI systems. Stakeholders must unwaveringly adhere to established ethical principles, ensuring transparency, fairness, and accountability at every stage of AI implementation.

When we delve into the realm of Gen AI, the potential for malpractice looms. Consider scenarios where a patient, with normal renal function manageable through medication, undergoes a renal scan. Unscrupulous use of Gen AI could manipulate images, creating false lesions, leading to unnecessary surgeries or even nephrectomy which can benefit illegal organ trade

Thus, the imperative lies in defining strong ethical boundaries, implementing robust audits, and establishing legal frameworks to prevent data manipulation and ensure the highest standards of integrity.

In embracing these technological advancements responsibly, we are not just witnessing the future of healthcare; we are actively shaping it. The era of proactive, preventive healthcare beckons, promising a future where wellness is at the forefront of the industry’s evolution.

The shift from traditional, one-size-fits-all medical practices, prone to misinterpretation and diagnostic errors, to AI-enhanced methodologies, heralds a new era of precision and personalised care. AI’s capability to analyse a broad spectrum of patient data—ranging from genetic backgrounds to lifestyle factors—promises a departure from misdiagnoses and introduces tailored therapeutic interventions.

AI and multimodal technologies enable a holistic view of the patient’s health, integrating diverse data points. While, Neuromorphic computing advances the portability of medical devices, including wearables and implants, transforming them into intelligent systems capable of adapting to varying conditions.

As thought leaders in the healthcare industry, our commitment to responsibly integrate these technologies paves the way for a future where healthcare is not only reactive, but anticipatory, personalised, and universally accessible.

Author

Anup S S
Practice Head, Artificial Intelligence, Tata Elxsi
Anup S.S. is a visionary in leveraging Artificial Intelligence, Machine Learning and Deep Learning. Leading breakthrough AI projects in healthcare, Anup’s strategic insight and innovation ignite client success, unlocking AI’s full potential.

Book Your Exclusive Print Copy Now!​

© 2024 TATA ELXSI
PRIVACY POLICY
COOKIE POLICY
TERMS OF USE
Facebook Twitter Youtube Linkedin Instagram Envelope

Great read FMF.

Oh wow, if this ends up being the Brainchip train, then buckle up because this could become one hell of a ride!

 

[DingoBorat]

How Nvidia, TSMC, Broadcom and Qualcomm will lead a trillion-dollar silicon boom - SiliconANGLE

How Nvidia, TSMC, Broadcom and Qualcomm will lead a trillion-dollar silicon boom - SiliconANGLE

siliconangle.com

It would be really nice, to get a partnership going with Nvidia..

They seem to be doing everything right at the moment.

This is a good video, if people have the Time to watch it..



Humanoid robots, are going to be a massive market, from 2025 onwards.

Car manufacturing, is a dying market and not a growth market, in my opinion.
It's been fraught with struggles, for many years, as can be seen with so much consolidation, over the last 40..

Even something as iconic as a Lamborghini, is just a fast VW with a badge.
Your luxurious Audi, is the same..
Hitler's Revenge (my Father's nickname for the VW fastback I learned to drive in) with fast glass.

Less and less people, will own or drive cars in the Future, even now, how many 5 seater cars, are driving around, with just one person in them?
With technology for business/entertainment and service industries, there are even less reasons to drive or own a car, let alone buy a new one, which quickly depreciates in value.

But humanoid robots, is a fresh new market.
Although incredibly complex, I think they are a much simpler to manufacture proposition, when supply chains and all the interconnected systems and features of an automobile are taken into consideration.

Elon Musk sees this and it's why he knows Optimus, will make Tesla a far greater and more successful Company, than by making EVs.

Hyundai owns Boston Dynamics, who are developing the new Atlas.

I can see companies like VW Group, Daimler AG, GM, Ford, Toyota etc also moving in this direction.
Not totally abandoning cars, but taking advantage of a fresh new and more profitable market.


Of course A.I. is the backbone of all this development and what's going to make it all "work".

Neuromorphic A.I. has an incredibly strong Future, in this field.

Hmm maybe just 5 more years ...
But the Foundation stones, will be laid "Tomorrow".

 

[goodvibes]

Neuromorphic Computing. “Brain on a chip”​

Brainchip got a mention….In addition to these tech behemoths, several startups are making waves in neuromorphic computing. BrainChip, for instance, has developed the Akida Neuromorphic System-on-Chip, which brings AI to the edge in a way that existing technologies are incapable of. The Akida chip is designed to provide a complete ultra-low-power AI Edge Network for vision, audio, olfactory, and innovative transducer applications (BrainChip, 2020).

Neuromorphic Computing. "Brain On A Chip".

Neuromorphic computing, a technology that mimics the neural structure of the human brain, is set to revolutionize information processing. This paradigm shift from traditional computing methods offers a new way to create efficient, powerful systems that can learn and adapt. The potential...

quantumzeitgeist.com

 

[Terroni2105]

Just a reminder Chippers, the ASX is closed tomorrow for a public holiday so there will be no trading

 

[Guzzi62]

How Nvidia, TSMC, Broadcom and Qualcomm will lead a trillion-dollar silicon boom - SiliconANGLE

How Nvidia, TSMC, Broadcom and Qualcomm will lead a trillion-dollar silicon boom - SiliconANGLE

siliconangle.com

​

​

Final thoughts on key topics​

Let’s close on some of the key issues we haven’t hit.

The future of AI and its market dynamics are evolving rapidly, with significant implications for key players and emerging technologies. Our analysis highlights the pivotal trends and forecasts that will shape the AI landscape over the next decade, focusing on AI inference at the edge, energy needs, geopolitical risks and the potential shifts in semiconductor manufacturing.

Key points​

• AI inference at the edge:
  • By 2034, 80% of AI spending is projected to be on inference at the edge.
  • This workload is expected to dominate the AI market.
  • While Nvidia currently holds a strong position in AI inference, driven largely by ChatGPT, the competitive landscape for high-volume, low-cost, low-power inference at the edge remains wide open and will challenge Nvidia’s dominance.

 

[DingoBorat]

​

​

Final thoughts on key topics​

Let’s close on some of the key issues we haven’t hit.

The future of AI and its market dynamics are evolving rapidly, with significant implications for key players and emerging technologies. Our analysis highlights the pivotal trends and forecasts that will shape the AI landscape over the next decade, focusing on AI inference at the edge, energy needs, geopolitical risks and the potential shifts in semiconductor manufacturing.

Key points​

• AI inference at the edge:
  • By 2034, 80% of AI spending is projected to be on inference at the edge.
  • This workload is expected to dominate the AI market.
  • While Nvidia currently holds a strong position in AI inference, driven largely by ChatGPT, the competitive landscape for high-volume, low-cost, low-power inference at the edge remains wide open and will challenge Nvidia’s dominance.

There's a joke there somewhere, I'm sure ..

 

[supersonic001]

Brainchip’s neuromorphic AI hardware accelerator at CES 2024 | Thomas Hülsing

Review of CES2024 — BrainChip Neuromorphic Computing https://lnkd.in/ehgW6SjC

www.linkedin.com

 

[Diogenese]

Brainchip’s neuromorphic AI hardware accelerator at CES 2024 | Thomas Hülsing

Review of CES2024 — BrainChip Neuromorphic Computing https://lnkd.in/ehgW6SjC

www.linkedin.com

View attachment 64580

I thought Todd could have done better when the interviewer was pushing for details of how it works.

He should have explained the difference between neuron spikes which add up to a threshold value and fire a spike, as compared to mathematical MACs.

Onsemi airbag deployment gets a mention.

 

[Labsy]

We can only hope that one of Sean important meetings was with Apples AI main man Mr John Giannandrea....This would be insane.

 

[Learning]

Not sure if this was shared. Two lectures from different universities talking about Akida.

Miss the copy of the link.

Learning

 

[Pmel]

https://www.capgemini.com/wp-content/uploads/2023/12/TechnoVision-2024-CTIO-Report-Web-Version.pdf

 

[Smoothsailing]

We can only hope that one of Sean important meetings was with Apples AI main man Mr John Giannandrea....This would be insane.

Imagine

 

[Diogenese]

View attachment 64583

Let's hope MF keeps all their BRN posts in a burn box.

Perhaps they could explain TeNNs to their readers.

 

[JoMo68]

Let's hope MF keeps all their BRN posts in a burn box.

Perhaps they could explain TeNNs to their readers.

Yes, I do believe that Mickelpenis is going to have some serious egg on his face at some point in the not too distant future

 

[DingoBorat]

Not sure if this was shared. Two lectures from different universities talking about Akida.

Miss the copy of the link.

View attachment 64584

Learning

Sounds like the professor, is impressed enough to become a shareholder in our Company.

We can't know how big his "bet" is though.

And anyway, what would he know?
He's probably just caught up in the "hype" like the rest of us ..

 

[MadMayHam | 合氣道]

Great read FMF.

Oh wow, if this ends up being the Brainchip train, then buckle up because this could become one hell of a ride!

 

[Pom down under]

Nice to see Tata Elxsi still on the neuromorphic train.

Multimodal AI and Neuromorphic AI: Detection, Diagnosis, Prognosis - Tata Elxsi

Discover how Multimodal and Neuromorphic AI lead healthcare from reactive to proactive. This article delves into Responsible AI and its transformative impact.

ai.tataelxsi.com

MULTIMODAL AI AND NEUROMORPHIC AI: DETECTION, DIAGNOSIS, PROGNOSIS

Navigate​

The synergy of cutting-edge technologies like Multimodal and Neuromorphic AI signals a pivotal shift from reactive to proactive healthcare. This article explores captivating use cases, offering insights on the implementation of Responsible AI.
Join us as we navigate the frontier of healthcare, where the synergy of innovation and responsibility promises a revolution in patient care and well-being.

Current state of AI adoption in Healthcare​

Unlocking the full potential of AI in healthcare is an uncharted journey. From optimising drug combinations to spearheading clinical correlation and toxicity studies, AI is set to redefine every facet of the industry. Despite its transformative capabilities, AI remains a niche technology, requiring a nuanced understanding of its application in healthcare.

The tides are changing as the healthcare sector recognises the urgency for an interdisciplinary approach, marrying engineering with medical science. This paradigm shift signals an imminent era where AI’s vast capabilities will revolutionise diagnostics, patient treatment, protocols, drug development and delivery, and prescription practices over the next decade.

Join us as we navigate the frontier of healthcare, where the synergy of innovation and responsibility promises a revolution in patient care and well-being.

Multimodal AI and Neuromorphic Technology – A new era in Preventive Healthcare​

In the ever-evolving landscape of healthcare, the amalgamation of Multimodal AI and Neuromorphic Technology marks a pivotal moment—a shift from reactive medicine to a proactive, preventive healthcare paradigm. This synergy is not just a collaboration of cutting-edge technologies; it’s a gateway to a future where wellness takes centre stage.

These technologies hold promise to transform healthcare by enhancing diagnostics, enabling personalised medicine, predicting long-term prognosis and contributing to innovations in therapeutic interventions.

Let’s delve into compelling use cases and glimpse the future of preventive healthcare.

The tides are changing as the healthcare sector recognises the urgency for an interdisciplinary approach, marrying engineering with medical science. This paradigm shift signals an imminent era where AI’s vast capabilities will revolutionise diagnostics, patient treatment, protocols, drug development and delivery, and prescription practices over the next decade.

Join us as we navigate the frontier of healthcare, where the synergy of innovation and responsibility promises a revolution in patient care and well-being.

Defining Multimodal and Neuromorphic AI​

Multi-modal AI​

Multimodal AI refers to the artificial intelligence systems that process and analyse data from multiple modalities or sources. In healthcare, these modalities often include both visual and clinical data. Visual data may include medical images from scans, while clinical data encompasses patient records, parameters, and test reports. Multimodal AI integrates these diverse data types to provide a comprehensive understanding, draw meaningful insights and give suggestions based on data and image analytics.

Neuromorphic Technology​

The term “neuromorphic” comes from the combination of “neuro” (related to the nervous system) and “morphic” (related to form or structure). Neuromorphic technology is an innovative approach in computing that draws inspiration from the structure and function of a human brain. These are AI powered by brain-like computing architectures. It can help process larger amount of data with less computing power, memory and electric power consumption. Neuromorphic Technology utilises Artificial Neural Networks (ANN) and Spiking Neural Networks (SNN) to mimic the parallel processing, event-driven nature, and adaptability observed in biological brains.

Multimodal Inputs​

• Medical Images
• Lab Reports
• Clinical History
• Patient Demographic Information

Fusion Module​

Cross-modal attention mechanism to dynamically weigh the importance of text, video, and other parameters for calculating index to decide priority of selection.

Inference Outputs​

Inference Results for

• Diagnostic
• Prognostic
• Lifestyle Recommendation
• Disease Prediction

Use Cases of Multimodal and Neuromorphic AI​

Early Screening & Disease Detection​

Multimodal AI​

• Integrates visual and clinical data for holistic analysis.
• Advanced –image recognition for early detection.
• Comprehensive patient profiling.

Neuromorphic Technology​

• Efficient pattern recognition for subtle disease indicators.
• Event-driven processing for real-time detection. This is crucial for detecting anomalies or irregularities that may be early signs of diseases.
• Continuous monitoring for dynamic changes. This continuous surveillance is especially valuable for conditions with varying symptoms.

Diagnosis​

Multimodal AI​

• Integrated diagnostic insights from diverse data.
• Cross-verification for reliability.
• Tailored treatment plans based on nuanced understanding.
• Continuous updates based on latest findings reported in the subject.

Neuromorphic Technology​

• Large and Efficient data processing with minimal energy consumption. This efficiency contributes to faster and more accurate diagnoses.
• Allows integration of more complex algorithms on wearable devices; makes diagnostics more real-time and helps timely interventions
• Implantable devices can be made AI enabled with Neuromorphic computing, due to the low computing requirement and power consumption, making the diagnosis and management more precise and real-time.
• Adaptive intelligence for dynamic adjustments. This adaptability enhances the precision of diagnostic processes. This event-driven processing aligns with the dynamic nature of healthcare data allowing for more accurate and timely diagnoses.
• SNN for real-time response
  and accuracy.

Prognosis​

Multimodal AI​

• Research Advancements: Facilitates discovery of new insights, contributing to medical advancements and innovations.
• Personalised Prognostic Models: Considering both visual and clinical data, these models account for individual variations, and correlate with prior case records and provide more accurate predictions of disease outcomes.
• Dynamic Adaptability: The adaptability of multimodal AI to changing data patterns ensures that prognostic models can dynamically adjust based on evolving patient conditions and improve prognosis predictions

Neuromorphic Technology​

• Analysis of longitudinal data for predicting disease progression.
• Dynamic adaptability in prognostic models that can adjust to changing data patterns. This adaptability improves prognosis prediction accuracy for evolving patient conditions.
• Personalised prognostic insights based on individual variations can help in more accurate predictions tailored to individual patient profiles.

Tata Elxsi Use Case​

Disease Detection and Diagnosis​

Utilising Neuromorphic Technology, we’ve achieved significant advancements in the analysis of medical images on low-computing embedded platforms, enabling on-field diagnostics of ultrasound images.

This innovative approach provides critical diagnostic information for musculoskeletal injuries, including tissue damage extent, recovery progress, and healing time predictions, all with enhanced efficiency and device portability making it ideal for applications such as sports medicine.

Applications of Multimodal and Neuromorphic AI​

Multimodal AI​

• Comprehensive Patient Analysis
• Diagnostic Accuracy
• Mental Health and Behavioural Analysis
• Lifestyle Reviews and Recommendations
• Management of Chronic Diseases like Diabetes/HT/Cardiac Diseases with continous monitoring and personalised medications
• Diagnosis/Management and Prognosis of various types of cancers, Digital Drug Trials, Effective Pandemic Surveillance and staging, Gene Therapy and Genomics
• Recommendations for interventions and Prioritisation of therapeutic resources and modalities

Neuromorphic Technology​

• Implants, Wearables Devices
• Processing Large Data
• Medical Imaging Analysis
• Drug Discovery and Personalised Medicine
• Robotic Surgery Assistance
• Neurological Disorder Understanding
• Patient Care and Rehabilitation
• Predictive Analytics for Healthcare Management
• Energy Efficient Remote Monitoring

The Synergy of MLOps and Advanced AI​

The transformative impact of MLOps across operational efficiency, data management, patient outcomes, and the overall quality of care is unmistakable. In the quest for advancing healthcare, the convergence of Machine Learning Operations (MLOps) with Multimodal and Neuromorphic AI has emerged as a game-changer. These technologies can help in seamless deployment, continuous monitoring, and collaborative development across various stakeholders in the healthcare ecosystem.

While Advanced AI Technologies offer the potential for improvising the use cases, the application of MLOps can be instrumental in strengthening and regulating these advancements. It achieves this by bringing in streamlined AI development processes, dataset management, continuous monitoring of model accuracy across different versions, ensuring the deployment of thoroughly vetted versions for clinical use.

Additonally, MLOps frameworks enable and learn from deviations, further enhancing their efficacy in healthcare applications.

Use Cases​

Disease Detection​

Disease Prediction and Prevention
Real World Application – Early Detection of Chronic Disease, Infectious Disease Monitoring
Healthcare Fraud Detection
Real World Application – Claims Analysis, Identify Theft Prevention
Medical Imaging Analysis
Real World Application – Early Cancer Detection, Neurological Disorders Diagnosis
Genomic Research
Real World Application – Cancer Genomics, Rare Genetic Diseases

Diagnosis​

Drug Discovery and Development
Real World Application – Protein Folding Prediction, Drug Toxicity Prediction
Personalised Medicine
Real World Application – Oncology and Targeted Therapies, Chronic Disease Management
Healthcare Resource Management
Real World Application – Emergency Room Digitalised Management, Pharmaceutical Supply Chain Management

Prognosis​

Prediction of Clinical Outcome
Real World Application – Prediction of recovery time and quality of life, Adverse Effects, Short term and long term impact
Remote Patient Monitoring
Real World Application – Chronic Disease Management, Post Surgery Monitoring

Responsible AI – Navigating Ethical Frontiers​

In the realm of AI, addressing bias stands as a pivotal ethical imperative, particularly in fields like medical analysis where the demand for precision is ethically, legally, and morally paramount. As AI practitioners, our commitment to responsible AI requires rigorous testing using diverse, unbiased anonymised datasets, continual monitoring to mitigate biases, and a steadfast dedication to achieving fair outcomes across diverse patient populations.

Moreover, the ethical considerations extend to the strategic utilisation of data. The foundation of responsible AI in healthcare is laid upon a robust ethical framework that guides the entire lifecycle of Neuromorphic and Multimodal AI systems. Stakeholders must unwaveringly adhere to established ethical principles, ensuring transparency, fairness, and accountability at every stage of AI implementation.

When we delve into the realm of Gen AI, the potential for malpractice looms. Consider scenarios where a patient, with normal renal function manageable through medication, undergoes a renal scan. Unscrupulous use of Gen AI could manipulate images, creating false lesions, leading to unnecessary surgeries or even nephrectomy which can benefit illegal organ trade

Thus, the imperative lies in defining strong ethical boundaries, implementing robust audits, and establishing legal frameworks to prevent data manipulation and ensure the highest standards of integrity.

In embracing these technological advancements responsibly, we are not just witnessing the future of healthcare; we are actively shaping it. The era of proactive, preventive healthcare beckons, promising a future where wellness is at the forefront of the industry’s evolution.

The shift from traditional, one-size-fits-all medical practices, prone to misinterpretation and diagnostic errors, to AI-enhanced methodologies, heralds a new era of precision and personalised care. AI’s capability to analyse a broad spectrum of patient data—ranging from genetic backgrounds to lifestyle factors—promises a departure from misdiagnoses and introduces tailored therapeutic interventions.

AI and multimodal technologies enable a holistic view of the patient’s health, integrating diverse data points. While, Neuromorphic computing advances the portability of medical devices, including wearables and implants, transforming them into intelligent systems capable of adapting to varying conditions.

As thought leaders in the healthcare industry, our commitment to responsibly integrate these technologies paves the way for a future where healthcare is not only reactive, but anticipatory, personalised, and universally accessible.

Author

Anup S S
Practice Head, Artificial Intelligence, Tata Elxsi
Anup S.S. is a visionary in leveraging Artificial Intelligence, Machine Learning and Deep Learning. Leading breakthrough AI projects in healthcare, Anup’s strategic insight and innovation ignite client success, unlocking AI’s full potential.

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Neuromorphic Computing Enhance Computing Efficiency | E-SPIN Group

Neuromorphic Computing is an innovative approach to design and build computer systems that are inspired by the structure and function of the human brain.

www.e-spincorp.com

E-SPIN
MONDAY, 10 JUNE 2024 / PUBLISHED IN GLOBAL THEMES AND FEATURE TOPICS

Neuromorphic Computing Enhance Computing Efficiency​

Neuromorphic Computing is an innovative approach to design and build computer systems that are inspired by the structure and function of the human brain. This field combines principles from neuroscience, computer science, and electrical engineering to create hardware and software that mimic neural processes. Here’s an overview of key concepts and developments in neuromorphic computing:

Excerpt.

Examples of Neuromorphic Computing in 2024​

1. IBM’s TrueNorth:
   • Architecture: TrueNorth chip features 1 million neurons and 256 million synapses, organized into 4,096 neurosynaptic cores. Each core simulates 256 neurons.
   • Applications: Used in image recognition, sensory processing, and cognitive computing tasks, demonstrating high efficiency and low power consumption.
2. Intel’s Loihi 2:
   • Self-Learning Capabilities: Loihi 2 chips continue to advance real-time learning and adaptation, reflecting brain-like plasticity.
   • Performance: Significant improvements in speed and scalability, facilitating more complex problem-solving and adaptive behaviors in real-time applications.
3. BrainChip’s Akida:
   
   • Event-Based Processing: Akida leverages event-based processing for energy-efficient, real-time data analysis.
   • Commercial Use: Applied in smart home devices, automotive systems, and industrial applications for enhanced pattern recognition and decision-making.
4. SynSense DYNAP-SEL:
   • Ultra-Low Power Consumption: Designed for ultra-low power applications, DYNAP-SEL is used in edge computing, enabling advanced processing capabilities in IoT devices without significant power draw.
   • Real-World Deployment: Implemented in sensor networks for real-time environmental monitoring, providing efficient data processing at the edge.
5. Human Brain Project’s SpiNNaker:
   • Large-Scale Neural Simulations: SpiNNaker (Spiking Neural Network Architecture) simulates large-scale neural networks, supporting neuroscience research and neuromorphic computing development.
   • Collaboration: Used in collaborative research projects to explore brain function and develop new neuromorphic algorithms and hardware.

ABOUT US​

Established in 2005, E-SPIN is a private enterprise representing Enterprise Solutions Professional on Information and Network. Serving as a regional hub in South East Asia (SEA) with operations spanning Malaysia, Singapore, Indonesia, Thailand, Philippines, and Vietnam, as well as in the Greater China Region (GRC) covering Hong Kong, Macau, and China, the company engages in international trade across adjacent nations.

• E-SPIN specializes in delivering innovative Enterprise ICT solutions, distribution, and international trade, along with shared services outsourcing (SSO). Through a collaborative approach with leading technology partners, E-SPIN offers a comprehensive suite of solutions encompassing solutions consulting, network and systems integration, portal development, application integration, product training, skill certification, project management, maintenance support, and outsourcing management services. These services are tailored to meet the diverse needs of partners, enterprises, government entities, and military clients, delivering holistic value-added solutions.
• 
  
• E-SPIN today refers to the global organisation, and may refer to one or more of the member firms of E-SPIN Group of Companies, each of which is a separate legal entity.

WHAT WE DO​

E-SPIN specializes in delivering a range of value-added services within the regions where the company operates. These services include:

• Enterprise Technology Product Distribution & Trading
• Solution Consultancy
• Solution Architecture
• Network / System Integration
• Global Sourcing and Turnkey Project Management
• Product/System/Technology Migration and Modernasation
• Product/project Training and Knowledge Transfer
• Product/Project Maintenance Support
• Shared Services and Outsourcing (SSO)
• Managed Services
• Application Security Testing (AST) as a Services (SaaS)
• Network Monitoring System (NMS) as a Services (SaaS)
• Anything as a Services (XaaS)
• 
  
• These offerings are tailored to meet the evolving needs of businesses and organizations, providing comprehensive solutions and support across various aspects of enterprise technology and operations.

ACHIEVEMENTS​

We offer enterprise technology solutions and products to channel partners, corporate entities, and government clients within the regions where E-SPIN operates. Additionally, we cater to international markets and undertake projects spanning multiple countries or on a global scale, provided they are commercially viable.

• In 2005 E-SPIN was founded
• In 2015 E-SPIN operated and served 11 countries channel partners and end clients across region E-SPIN do business.
• In 2020 E-SPIN celebrate 15 years in business, and business keep expanding. 100+ channel partners and end clients
• 300+ completed projects and keep growing