< Previous3 /9 10 5 TRL /9 O PEN ENDED ARTIFICIAL INTELLIGENCE, which is in the Prototype Stage, is the area of research concerned with building a new form of AI’s that are able to generate their own problems in order to discover new ways to solve them and thereby evolve to be more than the sum of their training or parts. Ultimately researchers want to imbue AI’s with their own “critical skills and thinking” which, like humans, they can tap into to help them overcome and solve problems that they haven’t encountered before or been specifically trained to solve. Recent breakthroughs include the use of simulated environments to give these AI’s free reign to create their own problems and increase the complexity of tasks, and then find new ways to solve and overcome them, and so far the results have been ground breaking. DEFINITION Open Ended Artificial Intelligence is a form of autonomous AI that is capable of designing and then solving its own problems. EXAMPLE USE CASES Today Open Ended AI is still constrained, for the most part, to the labs, but it is already easy to see why an AI which can see, assess, and then find new ways to solve a myriad of problems both at global scale and potentially millions or even billions of times faster than humans can would be an advantage - whether it’s in the cyber arena to find new ways to attack and defend systems, create new products, discover new medical treatments, or a billion other things. This is a technology whose uses are literally limitless. FUTURE TRAJECTORY AND REPLACABILITY Over the next decade interest in the field will continue to accelerate, and interest and investment will continue to grow at an accelerating rate, primarily led by organisations in the Technology sector. Eventually these AI’s will reach a point where they have the independent human-like ability to see, assess, and solve problems without needing training, which will bring about a new AI era and give regulators nightmares. While Open Ended AI is in the Prototype Stage, over the long term it will be enhanced by advances in Artificial Intelligence, Artificial General Intelligence, Artificial Super intelligence, Containment Algorithms, Explainable AI, Evolutionary Robotics, Simulation Engines, as well as Compute and Intelligence, but at this point in time it is not clear what it will be replaced by. MATTHEW’S RECOMMENDATION In the short to medium term I suggest companies put the technology on their radars, explore the field, establish a point of view, experiment with it, and forecast out the implications of the technology. 15 SECOND SUMMARY Accessibility Affordability Competition Demonstration Desirability Investment Regulation Viability 6 7 4 8 9 7 4 9 1971 2019 2020 2028 2032 STATUS PRIMARY GLOBAL DEVELOPMENT AREAS IMPACT STARBURST APPEARANCES: ‘22, ‘23, ‘24 OPEN ENDED AI EXPLORE MORE. Click or scan me to learn more about this emerging tech. 330311institute.com MRL2 /9 10 6 TRL /9 Q UANTUM ARTIFICIAL INTELLIGENCE, a GENERAL PURPOSE TECHNOLOGY, which is in the Concept Stage and very early Prototype Stage, is the field of research concerned with trying to merge the power of Quantum Computers with the power of Machine Learning and Neural Networks. Recently there have been a number of breakthroughs using Quantum Simulators to develop the first generations of powerful Quantum AI algorithms that, when Quantum Computers become powerful enough, will let researchers run massive matrix analyses, and create an on ramp to create the world’s first Artificial Super Intelligence machines. DEFINITION Quantum Artificial Intelligence is the marriage of traditional and new purpose built Artificial Intelligence methods and techniques with ultra-powerful Quantum Computers. EXAMPLE USE CASES Today we are using the first Quantum Artificial Intelligence prototypes to test the viability of new financial matrix and optimisation models, and refine them. In the future the primary use case for the technology will include any use case that is too large or complex for traditional computers to manage efficiently, including the processing of new cyber security models, drugs, financial risk models, optimisation models, and many more besides. FUTURE TRAJECTORY AND REPLACABILITY Over the next decade interest in the field will continue to accelerate, and interest and investment will continue to grow at an accelerating rate, primarily led by organisations in the Financial Services and Technology sector, with support from government funding, and university grants. In time we will see more organisations develop and test Quantum AI, and while it will be some time before it becomes widely adopted it is likely that the sheer performance of the technology will help accelerate its adoption. While Quantum Artificial Intelligence is in the Concept Stage and very early Prototype Stage, over the long term it will be enhanced by advances in Artificial Intelligence, Creative Machines, and Quantum Computers, but at this point in time it is not clear what will replace it. MATTHEW’S RECOMMENDATION In the short to medium term I suggest companies put the technology on their radars, establish a point of view, and re- visit it every few years until progress in the space accelerates. 15 SECOND SUMMARY Accessibility Affordability Competition Demonstration Desirability Investment Regulation Viability 6 2 2 4 9 3 2 8 1989 2009 2018 2025 2033 STATUS PRIMARY GLOBAL DEVELOPMENT AREAS IMPACT STARBURST APPEARANCES: ‘19, ‘20, ‘21, ‘22, ‘23, ‘24 QUANTUM ARTIFICIAL INTELLIGENCE EXPLORE MORE. Click or scan me to learn more about this emerging tech. 331311institute.com MRL1 /9 9 /10 3 TRL /9 Q UANTUM LANGUAGE PROCESSING, which is in the Concept Stage, is the field of research concerned with using the vast power of Quantum Computers and Quantum Artificial Intelligence to create new and massive Natural Language Processing models that are so deep and vast that they are not only able to converse with anyone in any language on any topic but they can do it with genuine human-like emotions and behaviours. Recently researchers in the field have created first generation models which show the promise of the technology to be far superior to anything that the world’s best bots, digital humans, and NLP models are able to produce. DEFINITION Quantum Language Processing is the use of Quantum Computers and Quantum AI to create emotional and sophisticated NLP models. EXAMPLE USE CASES Today researchers have been developing basic models using theory. In the future though the power of quantum computers, which are hundreds of millions of times more powerful that today’s computer systems, will be able to let AI have natural language conversations at massive scale in parallel that are indistinguishable from human conversations - including their emotional context, intonation, and tone. FUTURE TRAJECTORY AND REPLACABILITY Over the next decade interest in the field will continue to accelerate, and interest and investment will continue to grow at an accelerating rate, primarily led by organisations in the Technology sector. In time we will see the space mature to the point where it becomes the defacto technology and conversational Human-Machine interface which will then, naturally, lead to questions concerning privacy and its influence and role on society, especially as it will also help improve the quality of Synthetic Content. While Quantum Language Processing is in the Concept Stage, over the long term it will be enhanced by advances in Natural Language Processing, Quantum Artificial intelligence, Quantum Computing, Shallow Neural Networks, as well as Compute, but at this point in time it is not clear what it will be replaced by. MATTHEW’S RECOMMENDATION In the short to medium term I suggest companies put the technology on their radars, explore the field, establish a point of view, and re-visit it every few years until progress in this space accelerates. 15 SECOND SUMMARY Accessibility Affordability Competition Demonstration Desirability Investment Regulation Viability 4 4 3 6 9 6 3 9 2008 2010 2025 2029 2035 STATUS PRIMARY GLOBAL DEVELOPMENT AREAS IMPACT STARBURST APPEARANCES: ‘21 QUANTUM LANGUAGE PROCESSING EXPLORE MORE. Click or scan me to learn more about this emerging tech. 332311institute.com MRL3 /9 8 /10 5 TRL /9 S HALLOW NEURAL NETWORKS, which are in the Prototype Stage, is the field of research concerned with trying to create functional, small, and lean Artificial Intelligence (AI) models that are able to perform their tasks using minimal compute, memory, and network resources. Recently there have been a number of developments in the field including the development of biologically inspired AI models that are able to control and drive autonomous cars using neural networks that have only 19 neurons. While there are many research directions being explored at the moment it is not lost on researchers that biological organisms, unlike their modern AI equivalents, are often able to perform very complex tasks with minimal brain power or energy consumption. Also, when coupled with new AI and Neural Processing Units at the edges of the network the use cases and potential of the technology multiplies. DEFINITION Shallow Neural Networks are neural networks that only have one, or a very small number, of hidden layers. EXAMPLE USE CASES Today Shallow Neural Networks are being used at the edge of the networks to perform Deep Learning tasks such as processing different sensory inputs, including imagery and environmental data, which can then be analysed and actioned instantaneously without having to use or rely on networks or datacenters. The technology’s potential is almost limitless and in the future use cases will span every sector, from enabling Implanted Medical Devices and healthcare diagnostic tools to monitor patient well being and enable interventions when needed, all the way through to being used for entertainment purposes to create, for example, Synthetic Content. FUTURE TRAJECTORY AND REPLACABILITY Over the next decade interest in the field will continue to accelerate, and interest and investment will continue to grow at an accelerating rate, primarily led by organisations in the Aerospace, Defence, and Technology sectors. In time we will see Shallow Neural Networks embedded at the edge of every network and become ubiquitous, and regulators will have to work hard to understand the implications of AI everywhere. While Shallow Neural Networks are in the Prototype Stage, over the long term they will be enhanced by advances in Artificial Intelligence, as well as Compute, but at this point in time it is not clear what they will be replaced by. MATTHEW’S RECOMMENDATION In the short to medium term I suggest companies put the technology on their radars, explore the field, establish a point of view, and re-visit it every few years until progress in this space accelerates. 15 SECOND SUMMARY Accessibility Affordability Competition Demonstration Desirability Investment Regulation Viability 4 6 4 7 9 6 2 9 1974 1991 2014 2026 2034 STATUS PRIMARY GLOBAL DEVELOPMENT AREAS IMPACT STARBURST APPEARANCES: ‘22, ‘23, ‘24 EXPLORE MORE. Click or scan me to learn more about this emerging tech. SHALLOW NEURAL NETWORKS 333311institute.com MRL8 /9 8 /10 9 TRL /9 S IMULATION ENGINES, a GENERAL PURPOSE TECHNOLOGY, which is in the Productisation Stage, is the field of research concerned with finding new ways to develop better and more realistic simulations, cheaper and faster, which can then be used for a variety of use cases. recently there have been a number of breakthroughs in the space with the development of new Virtual Reality simulation engines that allow machines to render virtual worlds in real time, and dramatic improvements in the reality, both scientific and visual, of those environments. DEFINITION Simulation Engines are virtual platforms capable of dynamically modelling environments and events at high speed to accelerate learning and the development of new products. EXAMPLE USE CASES Today we are using Simulation Engines in a myriad of ways, including as an aid to Creative Machines, and using them to take sensor feedback from products in order to create better products, as well as using them as a primary way to develop safer autonomous vehicles and more dexterous robots, and run the first Quantum Artificial Intelligence simulations. In the future the primary use case of the technology will be to create highly engaging and interactive education and training programs, and act as a platform that allows researchers to speed up the training of AI models by factors of millions. FUTURE TRAJECTORY AND REPLACABILITY Over the next decade interest in the field will continue to accelerate, and interest and investment will continue to grow at a highly accelerated rate, primarily led by organisations in the Aerospace, Defence, Entertainment, Technology and Transport sectors. In time, as machines learn more about the dynamics and the physics of our world, they will take on more of the load and responsibility of designing and rendering simulated environments, similarly over time the technology will be enhanced by advances in Neural Interfaces which will allow humans and machines render and interact with simulations and immersive worlds in real time. While Simulation Engines are in the Productisation Stage, over the long term they will be enhanced by advances in Artificial Intelligence, Creative Machines, Neural Interfaces, and UHD Rendering Engines, but at this point in time it is not clear what they will be replaced by. MATTHEW’S RECOMMENDATION In the short to medium term I suggest companies put the technology on their radars, explore the field, establish a point of view, experiment with it, with a view to implementing it, and forecast out the potential implications of the technology. 15 SECOND SUMMARY Accessibility Affordability Competition Demonstration Desirability Investment Regulation Viability 8 4 5 9 9 6 3 9 1973 1982 1997 2013 2030 STATUS PRIMARY GLOBAL DEVELOPMENT AREAS IMPACT SIMULATION ENGINES STARBURST APPEARANCES: ‘19, ‘20, ‘21, ‘22, ‘23, ‘24 EXPLORE MORE. Click or scan me to learn more about this emerging tech. 334311institute.com MRL4 /9 9 /10 7 TRL /9 S WARM ARTIFICIAL INTELLIGENCE, which is in the Prototype Stage, is the field of research concerned with developing new ways for different collections of entities, such as Nano-Machines and robots, to intelligently collaborate and work together to achieve specific tasks. Recently there have been a number of breakthroughs in the field, especially with regards to how robots are able to manage and organise themselves and combine their capabilities to accomplish set goals, as well as helping control the collective behaviours of different Artificial Intelligence programs, which reduces the risk of their going rogue. DEFINITION Swarm Intelligence is the influence of collective behavioural traits and ethics in a decentralised, self organising natural or artificial system to sway collective behaviours and outcomes. EXAMPLE USE CASES Today we are using Swarm Artificial Intelligence to create the first generations of robots that are capable of coming together and organising themselves to accomplish specific goals. in the future the primary use of this technology will be to create better cyber security solutions, coordinate Nano-Machines within the human body, and create robot swarms capable of accomplishing a myriad of tasks. FUTURE TRAJECTORY AND REPLACABILITY Over the next decade interest in the field will continue to accelerate, and interest and investment will continue to grow at an accelerating rate, primarily led by organisations in the Technology sector, with support from government funding, and university grants. In time we will see the technology reach a point where machines are able to collaborate and coordinate with one another without the input of humans in order to achieve a myriad of goals. While Swarm Artificial Intelligence is in the Prototype Stage, over the long term it will be enhanced by advances in Artificial Intelligence, Creative Machines, Nano-Machines, Neurobiotics, and Robotics, but at this point in time it is not clear what it will be replaced by. MATTHEW’S RECOMMENDATION In the short to medium term I suggest companies put the technology on their radars, establish a point of view, and re- visit it every few years until progress in the space accelerates. 15 SECOND SUMMARY Accessibility Affordability Competition Demonstration Desirability Investment Regulation Viability 5 4 4 7 8 5 3 9 1989 1993 2003 2016 2036 STATUS PRIMARY GLOBAL DEVELOPMENT AREAS IMPACT STARBURST APPEARANCES: ‘17, ‘18, ‘19, ‘20, ‘21, ‘22, ‘23, ‘24 SWARM ARTIFICIAL INTELLIGENCE EXPLORE MORE. Click or scan me to learn more about this emerging tech. 335311institute.com MRL2 /9 8 /10 3 TRL /9 S YNTHETIC BIOLOGICAL INTELLIGENCE, a General Purpose Technology, which is in the early Prototype Stage, is the field of research concerned with developing new forms and systems of intelligence that are purely biological in nature. Today humans are considered the pinnacle of biological intelligence and Artificial Intelligence (AI) is considered to be the “pinnacle” of machine intelligence. Synthetic Biological Intelligence therefore is the development of new organic and biological forms of intelligence that are non-human and exist in different distinct formats and systems. Recent breakthroughs include researchers growing Brain Organoids - in dishes and jars - whose synapses and biological structures self-organised and “acquired” intelligence, such as the ability to play and win different games including Pong. Furthermore, in many cases, these new classes of biological intelligence have outperformed equivalent machine AI’s which is mind boggling in itself. DEFINITION Synthetic Biological Intelligence is the development of biological entities and systems capable of demonstrating intelligence, by whatever means, in their own right. EXAMPLE USE CASES While Biological Artificial Intelligence is still a nascent field it could one day be an incredibly powerful new form of so called “Wet Intelligence,” and given its properties therte’s no reason to believe that like traditional AI it couldn’t could be used almost anywhere and in any use case. FUTURE TRAJECTORY AND REPLACABILITY Over the next decade we will continue to see interest in this field accelerate, predominantly led by government grants. The thought of being able to develop biological intelligences that have capabilities superior to our own which can be transplanted at will into any system anywhere is incredibly attractive to some. But the technology still has a long way to go so it will be decades before it commercialises, then there will be the fractious ethical and regulatory hurdles it will have to overcome ... While Synthetic Biological Intelligence is in the early Prototype Stage over the longer term it could be enhanced by advances in 3D and 4D Bio-Printing, AI, Artificial Body Parts, Biological Computing and Electronics, DNA Neural Networks, Organic Computing, Organoids, Quantum Computing, Synthetic DNA, and other technologies, however over the long term it’s unclear what it could be superceeded by. MATTHEW’S RECOMMENDATION In the short to medium term I suggest companies put the technology on their radars, establish a point of view, and re- visit it every few years until progress in the space accelerates. 15 SECOND SUMMARY Accessibility Affordability Competition Demonstration Desirability Investment Regulation Viability 3 3 8 6 9 3 2 8 1964 1974 2021 2050 2068 STATUS PRIMARY GLOBAL DEVELOPMENT AREAS IMPACT SYNTHETIC BIOLOGICAL INTELLIGENCE STARBURST APPEARANCES: ‘23, ‘24 EXPLORE MORE. Click or scan me to learn more about this emerging tech. 336311institute.com MRL337311institute.comMATERIALSE VERYTHING IN the universe is made from something, whether it’s Dark Matter and vacuums, or the smartphones and devices in our hands, but as we continue to develop new materials that have increasingly intelligent and sophisticated characteristics we radically change the type of products we can design and create. The result of which means that we are increasingly able to create everything from invisibility cloaks to polymorphic robots, and let our trains of thought run free - thoughts that the first telepathic materials can amplify and merge with others. And we’re only just getting started with the material weirdness ... In this section you will find details of the emerging technologies that made it into this years Griffin Emerging Technology Starburst along with details of other impactful emerging technologies: 1.Aerogels 2.Atomic Knots 3.Auto-Cannabalistic Materials 4.Bio-Active Materials 5.Bio-Genic Materials 6.Bio-Materials 7.Bio-Mineralisation 8.Carbon Nanotubes 9.Chromogenic Materials 10.Digital Metamaterials 11.Electrocaloric Materials 12.Flexible Ceramics 13.Graphene 14.Infinitely Recyclable Plastics 15.Kinetic Materials 16.Living Materials 17.Magneto Restrictive Materials 18.Matter Creation 19.Mega Magnets 20.Meta-Optics 21.Metal Organic Frameworks 22.Metamaterials 23.Nano-Materials 24.Plasmonic Paints 25.Polymers 26.Polymorphic Liquid Metals 27.Programmable Matter 28.Quantum Materials 29.Re-Programmable Inks 30.Reactive Materials 31.Room Temperature Superconductors 32.Self-Healing Materials 33.Smart Materials 34.Sono-Inks 35.Spray On Materials 36.Super Alloys 37.Synthetic Diamonds 38.Telekinetic Metamaterials 39.Telepathic Metamaterials 40.Time Crystals 41.Vascularised Nanocomposites In addition to these emerging technologies there are many others that have yet to get an entry in this codex. These include, but are not limited to: 42.2D Materials 43.3D Printed Materials 44.Architectural Materials 45.Artificial Atoms 46.Bio-Ceramics 47.Bio-Compatible Materials 48.Bio-Glass 49.Bio-Inks 50.Bio-Plastics 51.Biodegradable Polymers 52.Carbon Fixing Materials 53.Designer Nanocrystals 54.Digital Materials 55.Electrochromic Materials 56.Embedded Logic Materials 57.Hybrid Particles 58.Hydrogels 59.Lavacrete 60.Liquid Armour 61.Liquid Light 62.Liquid Magnets 63.Liquid Metals 64.Living Metals 65.Magnetostrictive Materials 66.Metal Foam 67.Metallic Hydrogen 68.Nano-Ceramics 69.Nano-Photonic Materials 70.Optomechanics 71.Phase Change Materials 72.Polymetallic Complexes 73.Quantum Dots 74.Reprogrammable Materials 75.Semi-Conductors 76.Shape Changing Materials 77.Shape Memory Alloys 78.Sound Membranes 79.Stone Paper 80.Superfluids 81.Synthetic Materials 82.Thermo Bimetals 83.Thermoelectric Materials 84.Thermoplastic Polyurethane 339311institute.com BOOK AN EXPERT CALLNext >