When the Virtual and Digital worlds merge, a Digital Twin is created

An increasing number of companies are adopting a new emerging technology that allows the fusion of physical and virtual systems. The product of the fusion between these two components is called Digital Twin, a virtual representation of physical products and systems, generated through data collected from the environment. Creating a Digital Twin helps businesses to analyze more efficiently integrated sources of data and to predict how a product or system would behave in real life, enabling rapid and advanced decision making, or anticipating future risks.

An example of a successful Digital Twin application comes from the 36th America’s Cup race. Luna Rossa Prada Pirelli, in partnership with Altair, used Digital Twin technology to design, develop and optimize most of the boat’s components. Through the Digital Twin software powered by AI, Luna Rossa created a virtual representation of the boat to analyze how the AC75’s foils would impact the water, obtaining vital information on the boat’s structure and simulating how it would perform under different conditions.

Defender Emirates Team New Zealand also developed a Digital Twin in partnership with McKinsey and its subsidiary Quantum Black. Using AI and Machine Learning the team performed a vastly larger number of sailing maneuvers than a real crew could have done, in less time and processing a huge amount of data ten times faster than the normal testing process in a simulator room.

Digital Twin technology is also becoming a key component in the development of smart cities. Governments are partnering with tech companies to build a Digital Twin of physical cities to help them make better decisions in the design and development of urban areas or anticipate potential dramatic consequences from climate changes and make cities increasingly sustainable. Microsoft’s subsidiary Azure Digital Twins launched a comprehensive software solution to build digital smart cities, providing engineering companies and constructors with virtual representations of the physical spaces to help them analyzing the environment and optimizing space usage.

Accelerating technological advancements are paving the way for the adoption of Digital Twins, which are part of the most disruptive innovation cycle in the technology sector. The rise in popularity of Digital Twin technology is also supported by advancements in AI and the Internet of Things (IoT) which make it easier to collect and integrate a vast amount of data and information. According to Deloitte’s Tech Trends 2020 report, the global Digital Twin market was worth $3.8B in 2019, and it is projected to reach $35.8B by 2025, at a CAGR of more than 45%, creating an increasing number of long term investment opportunities.

---

The information in this article should not be regarded as a description of services provided by Delian Partners SA. The opinions expressed in this article are for general informational purposes only and are not intended to provide specific advice or recommendations for any individual or on any specific security or investment product. It is only intended to provide education about the financial industry. The views reflected in this article are subject to change at any time without notice.

Tech innovation is shaping the future of the healthcare sector

Historically driven by analog processes, healthcare activities have been significantly altered by the technology revolution. The increasing application of Artificial Intelligence (AI) systems is becoming more common in the healthcare industry and is helping healthcare businesses to be faster and more efficient.

Already pre-pandemic, about 80% of hospital leaders said cloud investments were a moderate, high, or critical priority for 2020. Going forward, the convergence of Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT) will further accelerate innovation adoption and related applications in the healthcare realm.

Technologies like Cloud computing, AI, IoT and machine learning are disrupting the health market and providing patients with new innovative services: for example, by engaging with digital providers, today patients can receive personalized medicines tailored to their specific needs, lifestyles, and genetic code, or can be visited by doctors directly through their smartphone.

Population aging around the world is another major tailwind for digital, patient-centric healthcare services. According to the World Health Organization (WHO), there were 703 million persons aged 65 years or over in the world in 2019. The number of older persons is projected to double to 1.5 billion by 2050. Technological advancements in screening processes, smartphones and wearables can bring point-of-care testing to the patients and represent a strong opportunity for providing sensitive, low-cost, rapid, and connected diagnostics.

There is increasing awareness that AI applications enable to analyze patient's health conditions and identify anomalies at a speed that humans cannot achieve, helping physicians to optimize and avoid time-consuming tasks, and reduce margins of error of diagnosis.

For example, today AI is already just as capable as (if not more capable than) doctors in diagnosing patients heart diseases, blood infections, and detect signs of potentially cancerous cellular growths. IBM’s AI program called Watson was recently challenged to analyze the genetic data of tumor cells. The human experts took about 160 hours to review and provide treatment recommendations based on their findings. Watson took just ten minutes to deliver the same kind of actionable advice.

Despite the rapid advancements in AI and machine learning in HC, we are still a long way from a total replacement of human intervention in medical processes. A research from Harvard showed that patients are reluctant to use health care provided by medical artificial intelligence even when it outperforms human doctors. The main reason is that patients believe that their medical needs are unique and cannot be adequately addressed by algorithms. For this reason, patients were less likely to utilize AI based services and wanted to pay less for it

The most likely evolution is that doctors will be supported by AI to perform repetitive tasks and increase quality of diagnosis at a fraction of time and costs. A recently developed machine-learning algorithm based on deep learning nearly matched the success rate of a human pathologist in interpreting pathology images, at about 96% accuracy. But the truly exciting thing was that combining the pathologist’s analysis the AI diagnostic method, the result improved to 99.5% accuracy,”

In summary: when it comes to healthcare, implementing AI solutions and machine learning will not necessarily mean replacing doctors, but optimizing and improving their abilities. The convergence between the healthcare industry with AI, Cloud computing, IT, and machine learning systems will further catalyze new innovative applications, providing patients with an early and accurate response to treatment and enabling healthcare organizations to reach new quality standards at a lower cost.

---

The information in this article should not be regarded as a description of services provided by Delian Partners SA. The opinions expressed in this article are for general informational purposes only and are not intended to provide specific advice or recommendations for any individual or on any specific security or investment product.  It is only intended to provide education about the financial industry. The views reflected in this article are subject to change at any time without notice.

Deep Learning algorithms are leading the next wave of technological disruption

The Defense Advanced Research Projects Agency (DARPA) recently announced that an AI algorithm piloting an F-16 Fighting Falcon in a simulated dogfight against a seasoned US AIR Force pilot achieved a flawless 5-0 win, with the human pilot never scoring a single hit. 

Regardless of how realistic “Computer vs. Human simulations” can be in such a complex environment, the most significant aspect is the fact that the specific AI system was developed less than one year ago using so-called “deep reinforcement learning”: starting with a complete lack of understanding about basic flight, the AI software autonomously learned fast, gaining the equivalent of 12 years of experience over the course of 4 billion simulations.

This AI revolution has been decades in the making, but only in the last decade have advances in computing power enabled a new era of AI training. The most recent technological disruption is enabled and accelerated by “Deep Learning”, the most advanced AI state in which machines can learn autonomously by analyzing vast amounts of unstructured data. 

As the cost of memory and compute came down dramatically, Deep Learning breakthroughs enabled computers to process a wide range of information across different and complicated data-driven applications, as in image recognition tools, speech recognition (NLP), image recognition (GPU), data discovery, and extraction. 

The era of Deep Learning, source Nvidia and JP Morgan

One of the most impressive examples of advances in machine learning is Alpha Zero, developed within the DeepMind division of Alphabet, which obliterated the highest-ranked chess program in the world (Stockfish). Given only the rules of the game, Alpha Zero learned how to play chess within four hours learning from playing against itself. The crucial advancement of this technology is the capacity for life-long learning: this AI system can acquire new information and keep in mind those already experienced to solve progressively more tasks, without losing information previously learned, a hurdle known as “catastrophic forgetting”.

The exponential advancements in AI models are supported by ever larger AI chips which are embedded with significant amounts of fast memory to handle the demands of AI algorithms. The race for manufacturing the largest AI chips includes both public and private companies: while Xilinx has announced the chip with the highest logic density on a single device ever bult, featuring 35bn transistors, private startup Cerebras has recently showcased the largest chip ever built, with 1.2 trillion transistors and 3000x more in-chip memory. Finally, Alphabet announced a major breakthrough in quantum computing, whereas their processor “Sycamore” took about 200 seconds to complete a task that would take a state-of-the art supercomputer approximately 10’000 years.

Regardless of potential winners and losers, technologic advancements are attracting large amount of investments. According to MarketsandMarkets, the overall Deep Learning market is estimated to reach $18.16B by 2023 from $3.18B in 2018, at a CAGR of 41.7% over this period. 

The investment implications are staggering and long-term in nature since companies are increasing technology investments to survive the rapidly changing landscape of the Fully Connected Economy. As a result, AI spending is projected to grow 28% annually from $19bn in 2018 to almost $100bn in 2023.

---

The information in this article should not be regarded as a description of services provided by Delian Partners SA. The opinions expressed in this article are for general informational purposes only and are not intended to provide specific advice or recommendations for any individual or on any specific security or investment product.  It is only intended to provide education about the financial industry. The views reflected in this article are subject to change at any time without notice.