Emerging Technologies SIG series – Spatial Computing and Creative Designs

Posted on February 28, 2023 by Peter Smulovics

To provide additional information related to the Emerging Technologies SIG of the FINOS/Linux Foundation, I start a miniseries of posts going deeper into some of the technologies mentioned there. If you are interested in participating, please add your remarks at the Special Interest Group – Emerging Technologies item on the FINOS project board.

The rise of spatial computing and the metaverse has opened up a new realm of possibilities for creative design. Spatial computing refers to the use of digital technologies to create experiences that integrate the physical and digital worlds, while the metaverse is a term used to describe a collective virtual shared space. In this post, we will explore how creative design is different in spatial computing and the metaverse compared to traditional creative design.

Firstly, spatial computing and the metaverse require a different approach to design. In traditional design, the focus is often on creating a visual representation of a product or experience. However, in spatial computing and the metaverse, the design process must consider the interaction between the user and the environment. This means that designers must think about how the user will move through the space, how they will interact with objects, and how they will engage with other users.

Secondly, spatial computing and the metaverse offer new opportunities for immersive experiences. Creative design in these contexts can involve the use of augmented reality, virtual reality, and mixed reality technologies to create interactive and engaging experiences that go beyond what is possible with traditional design. For example, a virtual art installation in the metaverse could allow users to explore and interact with the artwork in ways that would not be possible in the physical world.

Thirdly, spatial computing and the metaverse allow for a more collaborative and participatory approach to design. In traditional design, the designer creates a product or experience for the user, but in spatial computing and the metaverse, the user is an active participant in the design process. This means that designers must be open to feedback and willing to make changes based on user input. It also means that users can contribute to the design process by creating their own content and experiences within the metaverse.

Finally, spatial computing and the metaverse require a different set of technical skills and tools for creative design. In traditional design, designers may use tools like Adobe Photoshop or Illustrator to create visual designs, but in spatial computing and the metaverse, designers may need to use software like Unity or Unreal Engine to create interactive environments. Designers must also have a strong understanding of 3D modeling, animation, and game design principles.

In a quick summary, creative design in spatial computing and the metaverse offers new opportunities and challenges for designers. It requires a different approach to design, a focus on immersive experiences, a more collaborative process, and a different set of technical skills and tools. As these technologies continue to evolve, creative design in spatial computing and the metaverse will become increasingly important in creating engaging and memorable experiences for users. However, still as of today, spatial computing is an emerging field that combines digital technology with the physical environment to create new interactive and immersive experiences. So, what are some unique examples of creative design in spatial computing?

Virtual Real Estate: One of the most unique applications of spatial computing is the creation of virtual real estate. This involves creating digital spaces that can be bought and sold, just like physical real estate. These spaces can be used for a variety of purposes, such as virtual art galleries, music venues, or even digital storefronts for online businesses.
Augmented Reality Advertising: Augmented reality (AR) technology allows designers to overlay digital content onto the physical environment, creating an interactive and immersive experience. AR advertising, for example, can be used to create engaging and memorable experiences for customers. For example, a clothing retailer could create an AR app that allows customers to see how a particular outfit would look on them before making a purchase.
Virtual Museums and Galleries: Spatial computing allows designers to create immersive virtual museums and galleries that can be accessed from anywhere in the world. This not only makes art more accessible to a wider audience but also allows for new forms of engagement and interaction with the artwork. For example, virtual museums could allow visitors to interact with exhibits, providing additional information, or even allowing them to create their own artwork within the digital space.
Spatial Audio: Spatial audio is a technology that allows designers to create soundscapes that are tailored to the physical environment. This can be used to create immersive audio experiences that match the visual environment, creating a more complete sensory experience. For example, in a virtual reality game set in a forest, the spatial audio could be designed to make the player feel like they are actually surrounded by the sounds of nature.
Mixed Reality Performance: Mixed reality combines elements of virtual and physical reality, creating a seamless and interactive experience. In mixed reality performance, for example, performers can interact with virtual objects and environments in real-time. This allows for new forms of storytelling and audience engagement, creating a more immersive and interactive experience for the audience.

In conclusion, spatial computing provides designers with a new and exciting canvas for creative design. From virtual real estate to mixed reality performance, the possibilities for innovation and creativity are endless. As this technology continues to evolve, we can expect to see even more unique examples of creative design in spatial computing.

Emerging Technologies SIG series – What is cognitive AI (and how it is different than ChatGPT and co)

Posted on February 27, 2023 by Peter Smulovics

Cognitive AI and ChatGPT are two different types of artificial intelligence (AI) that operate in distinct ways. While ChatGPT is a large language model designed to generate human-like responses to textual prompts, cognitive AI is a more general term that refers to AI systems that are designed to emulate human cognitive functions such as perception, reasoning, and decision-making.

Cognitive AI is a type of AI that is modeled after the way that the human brain processes information. These systems are designed to recognize patterns, make predictions, and learn from experience, much like humans do. Cognitive AI systems can be used in a variety of applications, including speech and image recognition, natural language processing, and decision support.

One of the key differences between cognitive AI and ChatGPT is the scope of their abilities. While ChatGPT is primarily focused on generating human-like responses to textual prompts, cognitive AI systems are designed to be more flexible and adaptable, capable of handling a wider range of tasks.

Cognitive AI systems are typically more complex than ChatGPT, as they require advanced algorithms and data structures to support their functionality. They also typically require more data to train, as they need to learn from a wider range of inputs and experiences.

Another key difference between cognitive AI and ChatGPT is their level of explainability. ChatGPT generates responses based on statistical patterns found in large datasets, which can make it difficult to understand how it arrives at a particular response. Cognitive AI, on the other hand, is designed to be more transparent and explainable, with clear pathways for understanding how it arrives at its conclusions.

In terms of their applications, cognitive AI has a broader range of potential uses than ChatGPT. For example, cognitive AI can be used in healthcare to analyze patient data and make diagnoses, in finance to analyze market trends and make investment decisions, and in manufacturing to optimize production processes. While ChatGPT and cognitive AI are both forms of artificial intelligence, they operate in distinct ways and have different capabilities.

ChatGPT is primarily focused on generating human-like responses to textual prompts, while cognitive AI is designed to emulate human cognitive functions such as perception, reasoning, and decision-making. Cognitive AI is more complex and adaptable than ChatGPT, with a broader range of potential applications, but it also requires more data and is typically more transparent and explainable.

There are a number of examples of cognitive AI systems that are currently in use or in development. Some examples include:

IBM Watson: IBM Watson is a cognitive AI system that uses natural language processing and machine learning algorithms to understand and analyze large amounts of unstructured data, such as medical records, research papers, and social media posts.
Google DeepMind: Google DeepMind is a cognitive AI system that uses deep learning algorithms to analyze and interpret complex data, such as images and videos. It has been used in a number of applications, including healthcare, finance, and gaming.
Microsoft Cortana: Microsoft Cortana is a cognitive AI system that uses natural language processing and machine learning algorithms to understand and respond to user queries. It is integrated into a number of Microsoft products, including Windows and Xbox.
Amazon Alexa: Amazon Alexa is a cognitive AI system that uses natural language processing and machine learning algorithms to understand and respond to user requests. It is integrated into a number of Amazon products, including the Echo and Fire TV.
Tesla Autopilot: Tesla Autopilot is a cognitive AI system that uses machine learning algorithms to analyze data from sensors and cameras in order to navigate and control a vehicle. It is designed to assist drivers and improve safety on the road.

These are just a few examples of the many cognitive AI systems that are currently in use or in development. As the field of AI continues to evolve, we can expect to see even more sophisticated and powerful cognitive AI systems emerge in a wide range of industries and applications.

Cognitive AI is a rapidly evolving field, with new developments and advancements being made all the time. Here are some of the ways in which cognitive AI is expected to evolve in the near future:

Increased focus on explainability: As cognitive AI becomes more widely used, there is a growing demand for systems that are transparent and explainable. This means that AI systems will need to be designed in a way that allows humans to understand how they arrive at their conclusions and decisions.
Improved natural language processing: One of the key challenges in cognitive AI is developing systems that can understand and generate human language with a high degree of accuracy. As natural language processing technology continues to improve, we can expect to see more sophisticated and natural interactions between humans and cognitive AI systems.
Greater integration with human workers: While some people have expressed concerns about AI replacing human workers, many experts believe that cognitive AI will actually work in tandem with human workers, augmenting their abilities and providing new opportunities for collaboration.
Advancements in machine learning: Machine learning is a key component of cognitive AI, and ongoing research is expected to lead to new algorithms and approaches that improve the accuracy and effectiveness of these systems.
Applications in new industries and contexts: As cognitive AI continues to evolve, we can expect to see it being used in new industries and contexts, such as education, entertainment, and environmental monitoring.

Overall, the future of cognitive AI looks very promising, with ongoing advancements and developments opening up new possibilities for how we can use these systems to improve our lives and solve complex problems. However, it will be important to ensure that these systems are developed and deployed in a responsible and ethical manner, with careful consideration given to their potential impact on society and the environment. Going again over the pink clouds, downwards, while cognitive AI has made significant progress in recent years, there are still several limitations that need to be addressed in order for these systems to reach their full potential. Here are some of the current limitations of cognitive AI and the plans to overcome them:

Lack of transparency and interpretability: One of the biggest challenges facing cognitive AI is the lack of transparency and interpretability in how these systems arrive at their decisions. This makes it difficult for humans to trust and understand the results produced by AI systems. Researchers are working on developing techniques to increase transparency and interpretability, such as creating visualizations of the decision-making process or providing clear explanations for the reasoning behind a decision.
Data bias: Cognitive AI systems are only as good as the data they are trained on. If the data is biased or incomplete, the AI system will also be biased and incomplete. Researchers are working on developing techniques to address bias in data, such as collecting more diverse data and using algorithms that can detect and correct for bias.
Limited context awareness: Cognitive AI systems are currently limited in their ability to understand and interpret contextual information, such as social cues or situational factors. Researchers are working on developing techniques to improve context awareness, such as using deep learning algorithms to analyze context-rich data sources.
Computational limitations: Cognitive AI systems require a significant amount of computational power and storage capacity in order to function effectively. Researchers are working on developing more efficient algorithms and hardware to address these computational limitations.
Ethical considerations: The use of cognitive AI raises a number of ethical considerations, such as privacy, security, and bias. Researchers and policymakers are working on developing ethical guidelines and frameworks to ensure that these systems are developed and deployed in a responsible and ethical manner.

In conclusion, while there are still some limitations to cognitive AI, researchers and developers are actively working on developing new techniques and technologies to address these challenges. As cognitive AI continues to evolve, we can expect to see these systems become more sophisticated, accurate, and useful in a wide range of applications.

Announcing the 2023 Financial Services Autism Hackathon

Posted on February 24, 2023 by Peter Smulovics

Mission Statement

Bring developers from the Financial Services industry together to demonstrate how innovative Microsoft technologies can transform autism treatment by tackling real world use cases provided by families and creating lasting open-source projects for the community.

What does the Hackathon entail? It is:

2 days
70+ developers
5 use cases
- And infinity possibility to help people with using your technology skills!

Background

The first Financial Services Autism Hackathon was held in 2018 founded by Leo Junquera, working at Microsoft at the time, and Peter Smulovics from Morgan Stanley, as a grass roots effort to combine the unique value proposition of Microsoft (technology innovation) with the technology talent which exists in Financial Services, and direct it towards an industry in dire need of technology innovation.

Dates and Location

The date of the event is April 19-20^th. It is a hybrid event, with the in-person venue in New York City at the EY Wavespace.

Who should sign up?

It is a combination of roles that are filled by participants like yourself who make the hackathon successful each year.

Developers Work for the digital transformation to demonstrate the power how new technologies can be applied to the issues facing the autism community.
UX Designers People with Autism see the world differently – help them see the world! Design interfaces, interaction patterns, both in the digital and in the physical world, that enables them to overcome the differences and to participate in the digital transformation of the autism community!
Business Analysts Translate the use cases gathered from families with autistic children, behaviorists in the field, experts in the community, and service providers to enable better interaction between the parties, provide hallway test cases, helping finding the voice, to make the results presentable.

Registration information

If you are among the companies or individuals already pre-registered by domain/name, just visit the registration URL and log in with existing account or apply for a new account. If you are not, please do drop a note to Michelle.Ng@ey.com to add your company’s domain to the list 🙂 You can sign up as an individual or as a group. You will be aligned with a use case and a team and provided training resources if you need to prepare you for the event.

How should I prepare?

We will provide training sessions so that you are armed with the skills needed for success! The training will cover key technologies (Cloud, GitHub, IoT, Machine Learning, AI, etc.). We will also arrange Autism Awareness sessions relating to each of the use cases.

Use Cases

Use Case 1: IOT Data Collection

Dynamic programs require adaptations to data collection which are not supported by these systems. In the end teams frequently resort back to pen, paper, and mechanical devices such as hand tally counters. Can we take a different approach to this problem using an IoT pattern? Can we find a way to simplify data collection using simple mechanisms and use the cloud to store the data and classify it after the fact using modern tools and technologies? Can there be a repository of patterns to match the individual needs of learners and care providers?

Use Case 2: Transform Learner Analysis with Video and AI

Data collection is essential to successful outcomes for learners, but analysts are missing one of the most critical pieces of data when they are reviewing progress and creating programs, video of the learner. Social Media knows how important and compelling this information is but it is not being used to help people with Autism more effectively. This use case looks to develop a platform to capture this information, integrate it with data from other sources, and enrich the analysis with AI. This is an ambitious use case to transform the industry using a vital form of information which is currently missing.

Use Case 3: Using ML To Transform Learner Outcomes

There is a long-held belief that each individual case of autism is so unique that comparisons cannot be drawn to other cases, but marketing companies are able to use data to target the right message to us at the right time to sell their product. The therapies used to help learners with autism are data intensive. Can we use this data to transform outcomes for learners using modern technology and methods?

Use Case 4: HoloLens Skills Training

This use case will focus on the use of HoloLens for helping to teach job skills to people with autism, particularly those aging out of the supports provided in the school environment. Unemployment for those with autism is significant and the supports in the work environment are limited. The use case will use HoloLens augmented reality to help with basic job skills by presenting a visual cues on how to complete task such as stocking shelves or preparing food. This scenario should also address the issue of moving away from 1:1 support to a 1:n as an a student becomes an adult.

Use Case 5: Metaverse Social Practice

Developing social relationships with peers can be one of the biggest challenges for people with autism, despite a strong desire to form them. Social programs can be hard to practice due to limited opportunities, which can lead to disappointing outcomes in the few interactions, and stress can and frustration. The Metaverse offers an opportunity to practice social interactions in a fully immersive environment and the ability to formulate successful programs which can replicated at scale. This use case will lay the groundwork for a social interaction in the Metaverse between a subject and an AI generated peer.

Emerging Technologies SIG series – What is Space Technology and how it is relevant outside of Space?

Posted on February 23, 2023 by Peter Smulovics

Space technology has advanced tremendously over the past few decades and has become an essential tool for many industries. From satellite communications to weather forecasting, space technology has significantly impacted many sectors outside of the space industry. In this article, we will explore the relevance of space technology in various industries and look at some examples and case studies.

Communication

One of the most significant contributions of space technology to industries outside of space is in the field of communication. Satellites have revolutionized communication by making it possible to connect people across the globe. The use of satellites has enabled the provision of internet services, global positioning systems (GPS), and satellite phones. In remote areas where traditional communication methods are not available, satellite communication has become a critical tool for many industries.

Example: The Iridium satellite constellation is an excellent example of how space technology has impacted communication. The constellation consists of 66 satellites that provide voice and data communication services globally. The system has been used in several industries, including aviation, maritime, and government.

Agriculture

Space technology has also made significant contributions to the agricultural industry. It has enabled farmers to monitor crop growth, soil moisture, and weather patterns, leading to improved crop yields and reduced costs.

Example: The European Space Agency’s (ESA) Sentinel-2 satellite constellation is a prime example of space technology’s impact on agriculture. The satellites provide high-resolution imagery of agricultural land, which enables farmers to monitor their crops’ growth and health. This information helps farmers make better decisions on when to plant, water, and harvest their crops.

Disaster Management

Space technology has proven to be an essential tool in disaster management. Satellites provide crucial information that helps emergency responders make informed decisions during natural disasters such as hurricanes, earthquakes, and wildfires.

Example: During the 2010 earthquake in Haiti, satellite imagery was used to assess the damage and identify areas that required emergency assistance. This information was crucial in guiding the rescue and relief efforts.

Transportation

The use of space technology has also led to significant advancements in the transportation industry. Satellite data is used to monitor and manage traffic flow, improving road safety and reducing travel time.

Example: The global positioning system (GPS) is an excellent example of how space technology has impacted the transportation industry. GPS is used in navigation systems in cars, ships, and airplanes, making it easier for people to navigate and reach their destination.

Energy

Space technology has also contributed to the energy industry. Satellites provide data that helps energy companies locate new sources of energy and monitor their operations.

Example: The NASA Earth Observing System Data and Information System (EOSDIS) provides data that helps energy companies monitor their operations. The system provides data on land cover, vegetation, and weather patterns that help energy companies manage their operations effectively.

Space technology has made significant contributions to industries outside of the space industry. From communication to disaster management, the use of satellites has revolutionized various industries, improving efficiency, and reducing costs. The examples and case studies mentioned above show how space technology has made a positive impact on many industries. As technology continues to evolve, it will be interesting to see how space technology will continue to shape the future of these industries. So, you can probably understand why I wrote about digital twinning, 4D printing, even neural links before – but why space tech?

What about the financial industry?

Space technology has also impacted the finance industry in significant ways. Satellites are being used to provide critical data that helps financial institutions to make informed decisions on investments and risk management. The use of satellite technology has also led to the development of new financial products.

The use of satellite imagery and data has led to the development of crop insurance products for farmers. Insurance companies are using satellite imagery to assess crop yields and losses, which enables them to provide crop insurance products to farmers. This information helps farmers manage their risks and protect their investments.

Another example is the use of satellite data to track economic activity. Satellites can provide information on shipping, transportation, and manufacturing activities, which is useful in making investment decisions. Hedge funds and asset managers are using satellite data to gain insights into economic activity, giving them an edge in the market.

Space technology is also used in the banking sector. Banks are using satellite imagery and data to assess the risk of lending to certain areas. The data can provide insights into natural disasters, land use, and infrastructure, which is useful in assessing the risk of lending to a particular area.

In conclusion, space technology has revolutionized the finance industry, providing critical data that is useful in making investment decisions and managing risks. The use of satellite data is expected to increase as the technology continues to evolve, leading to the development of new financial products and services. The finance industry is just one example of how space technology is impacting various industries, and it is exciting to see how it will shape the future.

What does the future hold?

And again, not everything is in pink clouds 🙂 While space technology has made significant contributions to various industries, there are still some limitations and shortcomings that need to be addressed. Here are some of the current limitations and how they are being mitigated:

Cost: One of the main limitations of space technology is the cost associated with building, launching, and maintaining satellites. The cost of building and launching a satellite can be in the range of hundreds of millions of dollars, making it difficult for some industries to afford.

Mitigation: One way to mitigate the cost is through partnerships and collaborations. Several companies are partnering to share the cost of building and launching satellites. There is also a trend towards smaller, cheaper satellites, known as CubeSats, which are easier to build and launch. The use of reusable rockets, such as those developed by SpaceX, can also reduce the cost of launching satellites.

Technology Limitations: Space technology is continually evolving, and there are still some technological limitations that need to be addressed. For example, the current satellite communication technology has limitations in terms of bandwidth and speed.

Mitigation: The development of new technologies, such as quantum communication, could overcome some of these limitations. Quantum communication is a secure and fast method of communication that uses the principles of quantum mechanics.

Orbital Debris: The amount of space debris in orbit is increasing, which poses a threat to the operation of satellites and spacecraft. Orbital debris can collide with satellites, causing damage and potentially leading to the loss of the satellite.

Mitigation: Efforts are underway to mitigate the amount of space debris. Satellites are being designed with built-in propulsion systems that can help them avoid collisions. There are also initiatives to remove space debris from orbit, such as the European Space Agency’s Clean Space Initiative.

Data Security: The data transmitted through satellites is vulnerable to interception and hacking, which can pose a security threat to industries that rely on satellite technology.

Mitigation: The use of encryption and other security measures can mitigate the risk of data interception and hacking. There are also efforts to develop secure satellite communication systems, such as quantum communication, which are highly resistant to hacking.

In conclusion, while space technology has made significant contributions to various industries, there are still limitations and shortcomings that need to be addressed. Efforts are underway to mitigate these limitations through partnerships, the development of new technologies, and initiatives to reduce space debris and improve data security. As technology continues to evolve, it is expected that these limitations will be addressed, leading to further advancements in space technology and its impact on various industries.

Emerging Technologies SIG series – What is neural linking?

Posted on February 22, 2023 by Peter Smulovics

Neural links, also known as brain-computer interfaces (BCIs), are emerging technologies that enable communication between the human brain and an external device or system. These technologies have the potential to revolutionize fields such as healthcare, entertainment, education, and communication. In this article, we will explore the current state of neural links and examine some examples and case studies that demonstrate their potential.

They work by detecting and interpreting the electrical signals that are generated by the brain. These signals can be used to control external devices, such as computers or prosthetic limbs, or to receive sensory input, such as visual or auditory information. The most advanced neural links currently available are invasive, meaning that they require surgery to implant electrodes directly into the brain. However, there is ongoing research into non-invasive methods, such as using scalp electrodes or magnetic stimulation.

One of the most promising applications of neural links is in the field of healthcare. For example, neural links can be used to help patients with spinal cord injuries regain movement and control of their limbs. A study published in Nature in 2016 demonstrated that a patient with quadriplegia was able to control a robotic arm using a neural link, allowing him to perform tasks such as pouring water into a cup and stirring it with a spoon.

Another example of the potential of neural links in healthcare is their use in treating neurological disorders such as Parkinson’s disease. A study published in The Lancet in 2018 showed that patients with Parkinson’s who received deep brain stimulation via a neural link experienced significant improvements in their symptoms compared to those who received standard treatment.

Neural links also have the potential to transform entertainment and communication. For example, imagine being able to experience a movie or video game directly in your brain, without the need for a screen or speakers. This could be achieved through a neural link that delivers sensory input, such as visual and auditory information, directly to the brain. In 2018, a company called Neurable demonstrated a prototype of a virtual reality game that could be controlled using a neural link, allowing players to use their thoughts to interact with the virtual environment. Or imagine being able to log into an application, create and approve a financial transaction, etc. using just a neural link. Together with technologies like ChatGPT/GPT, this could open a new way of work, communication, life.

In the field of education, neural links could be used to enhance learning by providing students with personalized feedback and assistance. For example, a neural link could detect when a student is struggling with a particular concept and provide them with additional resources or support. In addition, neural links could be used to create more immersive and engaging educational experiences, such as virtual field trips or interactive simulations.

However, there are also concerns about the ethical and societal implications of neural links. One concern is the potential for neural links to be used for surveillance or mind control. Another concern is the potential for neural links to widen the gap between those who can afford the technology and those who cannot.

A company we cannot miss from any kind of compare on the topic in Neuralink – it is a company founded by Elon Musk in 2016 with the goal of developing neural links that are safe, affordable, and easy to use. Unlike most other neural link technologies, Neuralink aims to create a minimally invasive system that can be implanted in the brain without requiring major surgery. The system consists of tiny threads, thinner than a human hair, that are implanted using a custom robot. The threads are connected to a small device called the “Link” that is implanted behind the ear and can communicate wirelessly with external devices.

Neuralink’s ultimate goal is to enable humans to merge with artificial intelligence, creating a symbiotic relationship that enhances our cognitive abilities and enables us to keep up with the rapid pace of technological progress. While this vision is still a long way off, Neuralink has made significant progress in developing its technology. In 2020, the company demonstrated a prototype of its neural link system in pigs, showing that the technology is capable of transmitting signals from the brain to a computer. While there is still much work to be done, Neuralink has the potential to revolutionize the field of neural links and transform the way we interact with technology.

Despite the promise of neural links, and even beside the ones mentioned above, there are still several limitations and shortcomings that need to be addressed before they can become widely used. Some of these limitations and plans to remediate them are as follows:

Invasiveness: Most current neural links require invasive surgery to implant electrodes directly into the brain, which carries significant risks and limitations. Non-invasive methods, such as using scalp electrodes or magnetic stimulation, are being researched to overcome this limitation.
Scalability: Current neural links are limited in terms of the number of neurons they can record or stimulate at once. This limits their ability to provide precise and detailed control over external devices. Research is being conducted to develop more scalable systems that can record or stimulate a larger number of neurons.
Longevity: Neural links are currently limited in terms of their lifespan, as the electrodes can degrade over time or become displaced. Research is being conducted to develop more durable and longer-lasting materials for neural links.
Cost: Current neural links are expensive and not affordable for most people. Research is being conducted to develop more affordable and accessible neural link technologies.
Ethics: There are ethical concerns regarding the use of neural links, particularly regarding issues such as privacy, autonomy, and consent. These concerns need to be addressed to ensure that the use of neural links is ethical and does not violate individuals’ rights.

To address these limitations and shortcomings, ongoing research and development are being conducted in the field of neural links. Researchers are exploring new materials and technologies to make neural links more durable, scalable, and affordable. They are also working on developing non-invasive methods for implanting neural links and addressing ethical concerns related to their use. With continued research and development, it is expected that neural links will become more accessible, affordable, and practical for widespread use in the future. One way to make these limitations remediated faster is having open standards for neural links – at present, there are no widely accepted open standards for neural link technologies. Most companies and researchers in the field are working on proprietary systems that are not interoperable with one another. This lack of standardization can create issues such as limited compatibility between different systems and limited access to data.

However, there are efforts underway to establish open standards for neural link technologies. The IEEE Standards Association, for example, has launched a working group to develop a standard for brain-machine interface devices. The aim of this standard is to provide guidelines for designing, testing, and evaluating these devices to ensure that they are safe, effective, and reliable. The standard is being developed with input from experts in academia, industry, and regulatory agencies.

The creation of open standards for neural link technologies could have significant benefits for the field. It could increase interoperability between different systems, making it easier for researchers to collaborate and share data. It could also lead to more rapid innovation and development of new neural link technologies, as companies and researchers could build on existing standards rather than starting from scratch. However, the development of open standards will require collaboration and agreement among a wide range of stakeholders, including researchers, companies, and regulatory agencies.

Emerging Technologies SIG series – What is 4D printing?

Posted on February 21, 2023 by Peter Smulovics

3D printing has been a revolution in the world of manufacturing and engineering, enabling the creation of complex geometries and prototypes with unprecedented speed and precision. However, researchers and scientists have been exploring the possibility of taking 3D printing to the next level, and that is 4D printing. In this article, we will explain what 4D printing is, why it is important, and provide some examples of its use cases.

What is 4D Printing?

4D printing is a relatively new manufacturing technology that uses advanced materials and 3D printing techniques to create objects that can change their shape or functionality over time. The fourth dimension refers to time, as the printed object is designed to transform or self-assemble in response to an external trigger such as temperature, humidity, light, or magnetic field. These transformations can be either gradual or sudden, and they allow for the creation of complex structures that are difficult or impossible to achieve with traditional manufacturing methods.

One of the key features of 4D printing is the use of smart materials or shape-memory polymers, which can remember their original shape and recover it when exposed to a specific stimulus. These materials are often combined with 3D printing techniques, such as multi-material printing or 3D bioprinting, to create structures with intricate geometries and functionalities. The resulting objects can be used in a variety of applications, from medicine and robotics to architecture and aerospace.

Why is 4D Printing Important?

4D printing has the potential to revolutionize many industries and fields, by enabling the creation of structures that can adapt to their environment and perform multiple functions. Here are some reasons why 4D printing is important:

Greater design flexibility: 4D printing allows for the creation of objects with complex geometries and functions that are difficult or impossible to achieve with traditional manufacturing methods. This opens up new design possibilities for engineers and designers, allowing them to create objects that can adapt to changing conditions or perform multiple functions.

Self-assembly and self-repair: 4D printed objects can self-assemble or self-repair in response to external triggers, reducing the need for manual intervention or maintenance. This can be particularly useful in applications such as infrastructure or aerospace, where access and maintenance are challenging.

Customization and personalization: 4D printing can be used to create customized objects that are tailored to individual needs or preferences. This can be particularly useful in applications such as medicine or wearable technology, where personalized devices can improve patient outcomes or user experience.

Sustainable manufacturing: 4D printing can reduce waste and energy consumption by using smart materials and additive manufacturing techniques that require less material and energy than traditional manufacturing methods.

Use Cases and Examples of 4D Printing

Here are some examples of how 4D printing is being used in different fields:

Medicine: 4D printing is being used to create medical implants and devices that can adapt to the body’s changing needs. For example, a 4D printed stent can change its shape in response to blood flow or temperature changes, reducing the risk of complications or blockages. 4D printing is also being used to create bioprinted tissues and organs that can self-assemble and grow into functional structures.
Architecture: 4D printing is being used to create structures that can adapt to changing environmental conditions or user needs. For example, a 4D printed building facade can change its shape or transparency in response to sunlight or air quality, improving energy efficiency and user comfort.
Robotics: 4D printing is being used to create soft robots that can change their shape or stiffness in response to external stimuli. For example, a 4D printed gripper can adapt to the shape and size of the object it is picking up.

Limitations as of today of 4D printing

Life is not full on pink clouds: despite the potential of 4D printing, the technology is still in its early stages of development, and there are several limitations that need to be overcome to realize its full potential. Here are some of the current limitations of 4D printing and how they can be addressed in the future:

Material properties: 4D printing requires materials that can change their shape or functionality in response to external stimuli. However, the range of available smart materials is limited, and they can be expensive or difficult to process. To overcome this limitation, researchers are exploring new types of smart materials, such as shape-changing metals and alloys, or using multiple materials in a single print to create composite structures with unique properties.
Printing resolution: 4D printing requires high printing resolution to create objects with intricate geometries and functions. However, current 4D printers have limited printing resolution, which can affect the accuracy and reliability of the final product. To address this limitation, researchers are exploring new printing techniques, such as micro-scale 3D printing or multi-photon lithography, which can achieve higher printing resolution.
Trigger mechanisms: 4D printing requires an external trigger, such as temperature, humidity, or light, to activate the transformation process. However, the trigger mechanisms can be complex and difficult to control, which can affect the reliability and reproducibility of the printed object. To overcome this limitation, researchers are developing new trigger mechanisms, such as magnetic fields or acoustic waves, which can be more precise and controllable.
Scalability: 4D printing is currently limited to small-scale objects due to the complexity of the printing process and the materials used. However, for 4D printing to be widely adopted in industries such as construction or aerospace, it needs to be scalable to larger objects. To address this limitation, researchers are exploring new printing techniques, such as robotic printing or large-scale extrusion, which can achieve higher printing speed and scalability.

Conclusion

In conclusion, 4D printing has the potential to revolutionize many industries by enabling the creation of structures that can adapt to their environment and perform multiple functions. While the technology is still in its early stages, researchers are working to overcome the current limitations of 4D printing, such as material properties, printing resolution, trigger mechanisms, and scalability, to realize its full potential. As the technology advances, we can expect to see more innovative and practical applications of 4D printing in the future.

Emerging Technologies SIG series – What is Digital Twinning?

Posted on February 20, 2023 by Peter Smulovics

Digital twinning is a technology that is rapidly gaining popularity in the industrial world. It is a technique where a digital replica of a physical object is created, which is also known as a “twin”. This twin can be used for a variety of purposes such as simulation, analysis, and monitoring. With the advancements on the Internet of Things (IoT) and Artificial Intelligence (AI), digital twinning has become a promising tool that enables companies to make better decisions, optimize processes, and improve product quality.

Digital twins can be created for various things such as machines, buildings, cities, and even people. The purpose of creating a digital twin is to create a real-time replica of a physical object that can be monitored, simulated, and analyzed. This allows for more accurate and efficient decision-making processes.

One example of digital twinning is the manufacturing industry. Digital twins can be used to simulate production processes, analyze machine performance, and predict maintenance needs. By creating a digital twin of a machine, it is possible to monitor its performance, predict potential issues, and optimize its operations. This can lead to a reduction in downtime and an increase in overall efficiency.

Another example is the construction industry. Digital twins can be created for buildings, which can be used for planning, construction, and maintenance. This can help to reduce costs, improve safety, and optimize energy consumption. Digital twins can also be used for smart cities, where sensors and other IoT devices are used to create a digital replica of a city. This can be used to monitor traffic flow, optimize energy consumption, and improve overall city planning.

A notable case study of digital twinning is the use of digital twins in the aerospace industry. NASA has been using digital twins for several years to simulate the performance of spacecraft. By creating a digital twin of a spacecraft, it is possible to predict its behavior in different environments, simulate potential malfunctions, and optimize its design. This has helped NASA to reduce costs, improve safety, and increase the reliability of its spacecraft.

Another example is the use of digital twins in healthcare. Digital twins can be created for patients, which can be used to simulate the effects of different treatments and predict potential health issues. By creating a digital twin of a patient, doctors can make more accurate diagnoses and create more personalized treatment plans.

Challenges

While digital twinning is a powerful tool for businesses, it is not without its limitations. One of the major shortcomings of digital twinning is the need for high-quality data. Without accurate and reliable data, digital twins cannot provide the expected benefits. This can be a challenge for businesses that operate in complex environments or deal with large amounts of data.

Another challenge is the lack of standardization. There are currently no established standards for creating digital twins, which can lead to inconsistencies in the data and models used to create them. This can limit the interoperability of digital twins and make it difficult to share data across different platforms.

To address these limitations, there are plans to improve data quality and standardization. Some companies are investing in machine learning algorithms to improve the accuracy and reliability of data used to create digital twins. They are also exploring ways to standardize the creation and management of digital twins, including developing common data models and formats.

Another solution is to increase collaboration among businesses, academia, and government agencies to develop and share best practices for digital twinning. This can help to ensure that digital twins are created using consistent and reliable data, and that they can be easily integrated with other systems.

In addition, advancements in technologies like 5G networks and edge computing are expected to improve the reliability and speed of data collection and analysis, making it easier to create and manage digital twins.

Standards

Currently, there are no widely accepted open standards for digital twinning. However, there are efforts underway to establish standards and protocols for digital twinning to improve interoperability and facilitate data exchange among different systems.

One such effort is the Industrial Internet Consortium (IIC), a global organization that aims to accelerate the adoption of the Industrial Internet of Things (IIoT) by developing common architectures, frameworks, and protocols. The IIC has developed a reference architecture for digital twinning, which provides guidance on how to design, implement, and manage digital twins in a consistent manner.

Another organization that is working on standardizing digital twinning is the Object Management Group (OMG). The OMG is a not-for-profit organization that develops and maintains standards for distributed computing systems. They have created the Digital Twin Consortium, a collaborative community of organizations that are developing open-source software, frameworks, and standards for digital twinning.

In addition, various industry groups and standards organizations are also working on digital twinning standards. For example, the Institute of Electrical and Electronics Engineers (IEEE) has created a working group to develop standards for the interoperability of digital twins.

While there are currently no widely accepted open standards for digital twinning, the efforts of these organizations and industry groups are a step towards developing common frameworks and protocols for digital twinning. These standards will help improve interoperability and enable more efficient and effective use of digital twins in various industries.

In conclusion, digital twinning has the potential to transform businesses by improving decision-making and optimizing processes. While there are still challenges to be addressed, the industry is actively working on solutions to improve data quality, standardization, and interoperability. As these challenges are addressed, digital twinning is expected to become an even more powerful tool for businesses in a wide range of industries.

What are the current limitations of chatbots like ChatGPT?

Posted on February 17, 2023 by Peter Smulovics

Chatbots, such as ChatGPT, have revolutionized the way businesses interact with customers. They are computer programs designed to simulate human conversation and provide information, guidance, and support to users. While they have come a long way in recent years, there are still some limitations to their functionality.

Limited domain knowledge: While ChatGPT can generate responses to a wide range of topics, its responses may not always be accurate or relevant to the user’s specific needs. It lacks the domain-specific knowledge that human experts possess, making it challenging to provide personalized advice or help with complex issues.
Difficulty in understanding context: Chatbots have difficulty understanding the context in which a question or statement is made. They rely on pre-defined responses, making it challenging to respond appropriately to a user’s specific needs or queries that require additional clarification.
Inability to handle complex queries: Chatbots often struggle to handle complex queries or requests that require more significant processing power. They may not be able to provide users with the necessary information, leading to frustration and dissatisfaction.
Limited emotional intelligence: Chatbots lack the emotional intelligence that humans possess, making it challenging to detect and respond appropriately to a user’s emotional state. They may fail to recognize sarcasm, humor, or frustration, leading to inaccurate or irrelevant responses.
Inability to provide creative solutions: Chatbots are programmed to provide pre-defined responses based on a set of rules or algorithms. They lack the creativity required to provide novel solutions to complex problems, making it challenging to handle unique scenarios or situations.
Language limitations: While ChatGPT is designed to generate responses in various languages, it may not be able to understand all languages or dialects. This can limit its usefulness in regions where specific languages are predominant.
Limited memory: Chatbots have limited memory and can only remember information for a short period. This makes it challenging to provide continuity in conversations or remember previous interactions, making it difficult to personalize the user experience.

However, far from everything lost – as technology continues to advance, there are several limitations of chatbots that are likely to be overcome in the future. Here are a few limitations that are expected to be addressed (some of them pretty soon):

Improved Natural Language Processing (NLP): Natural Language Processing is the backbone of chatbots. As NLP technology advances, chatbots will become better at understanding and interpreting language, which will enable them to handle complex queries, understand context, and provide more personalized responses.
AI learning and training: Chatbots will be able to learn from their interactions with users and improve their performance over time. AI learning and training will enable chatbots to understand a user’s behavior and preferences, which will help them provide more personalized and relevant responses.
Emotion recognition: As chatbots become more advanced, they will be able to recognize a user’s emotional state and respond accordingly. This will help chatbots provide more empathetic and human-like responses to users.
Integration with other technologies: Chatbots will be able to integrate with other technologies like augmented reality, voice assistants, and smart home devices. This integration will enable chatbots to provide more comprehensive solutions to users and create a seamless user experience.
Memory and personalization: Chatbots will be able to store information about users and their preferences, which will enable them to provide personalized responses and recommendations. Chatbots will also be able to remember previous interactions, providing a seamless and personalized user experience.

Overall, as technology continues to advance, chatbots are expected to become even more advanced and useful, overcoming many of their current limitations. This will make chatbots an even more valuable tool for businesses looking to provide exceptional customer service and support. And who knows, one day, my blog might be written by a chatbot as well 😀

Mentors and sponsors – why both?

Posted on February 16, 2023 by Peter Smulovics

This post is inspired by an amazing TED talk snippet from the breathtaking Carla Harris. So, I already wrote about mentors, and you might have even started to look for one based on it – and now I come with this “sponsor” thing, looks like I cannot make up my mind? So, what is actually a sponsor? When and why do you need one?

Big multinational companies offer numerous opportunities for professional growth, but they can also be complex and challenging work environments. For this reason, having a sponsor can be incredibly valuable. A sponsor is an influential person within the organization who advocates for and supports a protégé’s career advancement. In this post, I will explore the value of having a sponsor if you work at a big multinational company.

Increased Visibility

One of the most significant benefits of having a sponsor is increased visibility within the organization. Sponsors are usually high-ranking executives or senior leaders who can offer valuable exposure to their protégés. This increased visibility can help you gain recognition and establish a reputation for excellence. Moreover, it can also provide valuable networking opportunities, which can lead to more significant opportunities and promotions.

Access to Resources

Working at a big multinational company can be challenging, and it can be challenging to navigate the various departments, teams, and stakeholders. A sponsor can help you gain access to resources that are critical to your success, such as training programs, mentorship opportunities, and important information about the company culture and policies. Sponsors can also offer guidance on how to navigate the complex organizational structure and offer insights on how to manage competing priorities and stakeholders.

Accelerated Learning

Another valuable benefit of having a sponsor is accelerated learning. A sponsor can offer invaluable advice and guidance, drawing from their own experience to help you avoid common pitfalls and achieve your career goals more efficiently. They can also help you identify opportunities for growth and development and provide insights into the skills and competencies needed for advancement within the company.

Advocacy and Support

A sponsor’s most significant value is their advocacy and support for their protégé’s career advancement. Sponsors can use their influence and network to open doors and create opportunities that might otherwise be inaccessible. They can also provide valuable feedback and offer guidance on how to develop critical skills and competencies that are necessary for success within the organization.

Increased Engagement and Retention

Finally, having a sponsor can help increase engagement and retention within the organization. Employees who feel supported and valued are more likely to be committed to their work and stay with the company long-term. Furthermore, sponsors can help employees understand how their work fits into the larger organizational goals and provide insights on how to make a more significant impact.

So, sponsor or mentor?

The short answer is: Both. In addition to the benefits of having a sponsor, it is important to understand how having a sponsor differs from having a mentor. While there is overlap between these roles, there are some key differences to consider.

A mentor is typically someone who provides guidance and advice on a broader range of topics, including personal and professional development. Mentors can come from within or outside of the organization, and they often have expertise in a particular field or industry. Mentors may provide support, feedback, and encouragement, but they are not necessarily in a position to advocate for their mentees within the organization.

A sponsor, on the other hand, is someone who actively supports and advocates for their protégé’s career advancement within the organization. Sponsors are typically higher up in the organization and have the influence and power to open doors and create opportunities for their protégés. Sponsors also provide critical feedback, guidance, and support, but their focus is on helping their protégés achieve their career goals within the organization.

In summary, while both mentors and sponsors provide valuable support and guidance, their focus and scope differ. Mentors provide guidance on personal and professional development, while sponsors focus specifically on career advancement within the organization. It is essential to understand the differences between these roles and identify which one is best suited for your specific career goals and needs. In some cases, having both a mentor and a sponsor can be beneficial, as they can provide complementary support and guidance in different areas of your professional development.

Conclusion

In conclusion, having a sponsor is incredibly valuable, especially for employees working at big multinational companies. Sponsors can provide increased visibility, access to resources, accelerated learning, advocacy and support, and increased engagement and retention. If you are looking to advance your career and make a more significant impact within your organization, it is essential to seek out a sponsor who can help guide and support your professional growth.

Thank you for coming to my TED talk 😉

Holograms, and the so many holographic displays and headsets

Posted on February 15, 2023 by Peter Smulovics

Get the basics out first, as I can always hear confusion between these two techs: Holograms are three-dimensional images that are created by light interference patterns. To make a hologram, a laser beam is split into two beams that pass-through lenses to expand them. One beam (the reference beam) is directed onto high-contrast film. The other beam is aimed at the object (the object beam). The light from the object beam reflects off the object and onto the film, where it interferes with the reference beam. The film records the interference pattern, which contains information about the shape, color, and texture of the object.

How to tell you are working with Spatial Computing without telling people you are working with Spatial Computing? I hid altogether more than a dozen different headsets on the picture 😀

On the other hand, holographic displays and headsets are devices that can project holograms into the real world or onto a transparent screen. There are different types of holographic displays and headsets, but one common method is to use a high-definition or 4K screen to reflect digital content through glass with special coating, called the glass optics. When placed at a certain angle, the glass optic will create an illusion that makes your brain interpret the digital content as three-dimensional. Another method is to use light projection to create digital objects that appear to float in midair. Holographic headsets like HoloLens use color sequential, see-through RGB displays to render holograms. The headsets also have sensors and cameras that track the user’s head movement and the environment and adjust the holograms accordingly.

There are different types of holographic displays and headsets, and they have different features, advantages, and disadvantages. Some of the factors that can be used to compare them are:

The distinction between AR and VR: AR headsets like HoloLens overlay digital content onto the real world, whereas VR headsets like Windows Mixed Reality immerse the user in a virtual environment.
The field of view: This is the extent of the visible area that the user can see through the device. Some holographic displays and headsets can provide a wide field of view, such as 90 degrees or more, while others have a narrower field of view, such as 40 degrees or less.
The size and weight: This affects the comfort and portability of the device. Some holographic displays and headsets are bulky and heavy, while others are thin and light, like sunglasses.
The price: This reflects the affordability and accessibility of the device. Some holographic displays and headsets are very expensive, costing thousands of dollars, while others are more affordable, costing hundreds of dollars or less.
The platform and compatibility: This determines the software and hardware requirements and the availability of content and applications for the device. Some holographic displays and headsets run on specific platforms, such as Windows or Android, while others are more open and compatible with various devices and systems.

So, what should you buy? Here is a table of comparison of some of the most popular holographic displays and headsets:

Name	Type	FOV	weight	Price	Platform
Oculus Quest 2	VR	90 degrees	503 g	$299	Android, Oculus app
Microsoft HoloLens 2	AR	52 degrees	566 g	$3,500	Windows 10, Azure
Magic Leap One	AR	50 degrees	316 g	$2,295	Lumin OS, Magic Leap app
Epson Moverio BT-300	AR	23 degrees	69 g	$699	Android, Moverio app
Google Glass Enterprise Edition 2	AR	20 degrees	46 g	$999	Android, Google app
Raptor AR headset	AR	13.5 degrees	98 g	$699	Android, Raptor app

There are numerous more out there, like the Nvidia Holographic VR, which is a technology that uses a holographic optical element to project a light field onto the user’s eyes, creating a 3D effect without the need for eye tracking or lenses. It is still in development and aims to improve the realism, comfort, and immersion of VR, or the Ikin Ryz, which is a device that connects to a smartphone and creates a holographic image that can be seen and interacted with by multiple users without glasses. It uses a patented light field technology and a nanochip to generate the holograms. For large scale

And what you should be careful about? There are many different display technologies, and they have different characteristics, advantages, and disadvantages. Some of the factors that can be used to compare them are:

The screen shape: This affects the viewing angle, the aspect ratio, and the distortion of the image. Some display technologies have flat screens, while others have curved screens, such as spherical, cylindrical, or concave.
The screen size: This affects the resolution, the brightness, and the power consumption of the display. Some display technologies can produce very large screens, while others are limited by physical or technical constraints.
The screen type: This affects the color reproduction, the contrast ratio, the response time, and the refresh rate of the display. Some display technologies are emissive, meaning they produce their own light, such as OLED, Plasma, and MicroLED, while others are transmissive, meaning they rely on a backlight, such as LCD and its derivatives.
The screen quality: This affects the image clarity, the brightness uniformity, the viewing angle, and the color accuracy of the display. Some display technologies have higher quality screens than others, depending on the pixel density, the subpixel arrangement, the color gamut, and the backlight technology.
The screen durability: This affects the lifespan, the reliability, and the environmental impact of the display. Some display technologies are more durable than others, depending on the material, the manufacturing process, the power consumption, and the susceptibility to burn-in, dead pixels, or image retention.