Fetch.AI has appointed Professor Michael Wooldridge, a world-leading artificial intelligence (AI) researcher and Head of the Department of Computer Science at the University of Oxford, to its team as an advisor. Professor Wooldridge will contribute his wealth of experience to the development of Fetch.AI’s revolutionary multi-agent deployment framework.
Wooldridge, Professor of Computer Science and Senior Research Fellow at Hertford College, University of Oxford, has been researching multi-agent systems for over 25 years. He has published more than three hundred articles on the theory and practice of autonomous agents and multi-agent systems.
Humayun Sheikh, CEO and Founder of Fetch.AI, said, “we are really pleased to have someone of Michael’s calibre and experience on the team. His understanding of multi-agent systems is unrivalled and we are really looking forward to seeing how his insights will shape our economic internet of the future. We aim to deliver a platform that will make decentralised intelligence for today’s on-demand world and we are confident that having Michael on board puts us in a strong position to do so.”
Professor Michael Wooldridge added, “The dream of software agents working on our behalf has been around for 20 years, but it is only now becoming possible because of the availability of universally networked smartphones and computers. The vision of Fetch.AI is to make multi-agent systems a commercial success. I am delighted to be able to work with them on making this a reality.”
Professor Wooldridge will work with the Fetch.AI team and an existing team of advisors to build a decentralised network that uses a convergence of AI, blockchain, and multi-agent systems to drive autonomous decision-making. Running on Fetch.AI’s blockchain and high performance smart contract framework Fetch.AI plans to deliver an environment where multi-agent systems can be brought to life. Such a system can potentially be a game-changer for the new digital economy.
About Fetch.AI Fetch.AI delivers a groundbreaking economic internet that enables emergent solutions to complex problems. It does this by enabling the deployment of multi-agent systems over a decentralised network and provides tools to enable the construction of intelligent agents. Fetch.AI delivers a unique, decentralised digital world that adapts in real-time to enable effective, friction-free value exchange. Powered by innovations such as the smart ledger, Fetch.AI has digital intelligence at its heart: delivering actionable predictions, instant trust information, enabling the construction of powerful collaborative models, improving efficiencies and streamlining processes.
Four events are slated in April and May to highlight the Challenge’s European PACE Tour
SANTA CLARA, CALIF., April 8, 2019 — The Trusted IoT Alliance (TIoTA), an ecosystem of more than 50 companies working together to build open source trusted systems for IoT with blockchain and related technologies, today announced the competitors for its Smart E-Mobility Challenge. The competitors are:
“We are thrilled with the level of interest the TIoTA challenges have sparked in the blockchain and IoT market spaces,” said Alexy Khrabrov, Chief Community Officer of the Alliance. “We received submissions from progressive technology organisations around the world, and each entrant will develop an overall reference architecture for their specific concept. Our goal with the challenge is to help the participants create solutions that they can then bring to market.”
MachNation, a global independent IoT research and benchmarking firm is co-producing the Challenge. TIoTA conceived the E-Mobility Challenge to spur the development and commercialisation of the emerging marketplace for e-mobility. The competition will leverage the ecosystem of services that already exists for electric vehicles (EVs) in Europe. The primary vehicle selected for the challenge is a Jaguar I-PACE, provided by Bosch.
The highlight of the Challenge is the PACE Tour, in which the Jaguar is available in select European cities for competitors to access, work on their solutions and conduct hackathons. The PACE Tour kicked off at the T-Labs and T-Systems Blockchain Mobility Hackathon in February. Upcoming PACE Tour dates include:
The winning entry will provide the most innovative solution that exemplifies progressive automation and connectivity features by enabling the Jaguar to discover charging stations, pay for electricity, discover additional services, and so on. The judges will determine which entries accomplish the following specific goals of the Challenge — a) interoperability, b) ease of use, and c) the ability to pay for electricity on the single invoice.
MachNation is the only firm exclusively dedicated to testing and researching Internet of Things (IoT) platforms, middleware, and services. MachNation owns and runs MachNation IoT Test Environment (MIT-E), the industry’s only independent, hands-on, benchmarking lab for IoT platforms. MachNation specializes in understanding and predicting IoT technologies including their impact on digitization, hardware, communication services, applications and support tools. MachNation participates in many of the world’s most exclusive IoT events and contributes regularly to leading IoT and business press. For more information, please visit https://www.machnation.com.
TIoTA is a result of the collaboration amongst passionate technologists working to leverage blockchain infrastructure to secure and scale IoT ecosystems. TIoTA seeks to enable trust in the data produced by such IoT systems in a distributed ledger/blockchain agnostic fashion, thereby enabling a decentralized trust model for interoperable digitized identities of physical goods, documents, immobilized assets, sensors, and machines. Visit trusted-iot.org and search #tiotachallenges and #tiotamobility to learn more.
Quotes from participating organisations “The Internet of Things, or IoT, is a network comprising billions of web-enabled devices. Even now, these devices are everyday companions. As a participant in the E-Mobility Challenge of the Trusted IoT Alliance, we are exploring the future potential of blockchain and similar distributed ledger technological approaches for the future of mobility. The work, together with international partners, is inspiring, and all companies and start-ups will gain valuable insights to continuously improve the security of IoT devices and increase users’ trust in IoT solutions.”
– Peter Busch, Product Owner, DLT Mobility at Bosch.
“We are very excited to take part in TIOTA’s Smart E-Mobility challenge. This challenge is the first step towards bringing awareness to the new digital economy for mobility. At Fetch.AI, we’re developing an intelligent blockchain framework that enables autonomy in the machine-to-machine ecosystem. As part of this hackathon we will demonstrate how autonomous agents can transact independently of human intervention to facilitate electric car charging, parking, and route optimization. Imagine. Build. Fetch.” – Maria Minaricova, Head of Business Development at Fetch.AI.
“As the world leader in blockchain infrastructure security, Ledger is proud to be part of the Smart E-Mobility Challenge proposed by the Trusted IoT Alliance. The use case Ledger wants to demonstrate is how to secure, attest and authenticate the data generated by a car and how to securely register it into a blockchain. Ledger’s secure solution will also give capability to the car to exchange green energy with the charging station using blockchain technology. Ledger is leveraging its open source operating system running on top of a certified EAL5+ secure element, ensuring best in class security for this technology. This solution is a must-have for all future IoT services using blockchain, in order to trust and trace data generated by any type of
IoT devices. Trusted IoT Alliance, through its endorsement of blockchain technologies to power the security of IoT products, is promoting the need of trustworthy into IoT data. Ledger with its unique technology of hardware oracle attesting and securing remote data at the collection point is promoting a secure IoT ecosystem based on secure element connected with any type of blockchain.” – Eric Larchevêque, Chief Executive Officer at Ledger.
“MachNation is excited to see so many participants in this emerging market. We look forward to seeing the applications and ideas that come from this creative group of technology developers.” – Steve Hilton, President of MachNation.
“The Trusted IoT Alliance is a great example of collaboration across industry boundaries to jointly share the future of mobility.” – Dietrich Sümmermann, Chairman of the Share&Charge Foundation.
“It’s great to be part of the challenge. We will bring together the strength of all our partners and the distributed ledger technologies. I’m very much looking forward to see exciting solutions.” – Prof. Dr. Monika Sturm, Principal at Siemens AG.
“Streamr is delighted to be showcasing how a decentralized blockchain-powered real-time data marketplace can optimize the road network and increase safety. We are stimulating a data economy with other TIoTA members Fetch.AI who subscribe to the data from the sensors in the Jaguar iPace. This feed shares events such as accidents with other subscribing cars and those running the road infrastructure who can change traffic lights, speed limits and set up diversions more quickly. Drivers can benefit from data sharing by receiving monetization when aggregated data is sold. Riddle and Code’s hardware-based car wallet make the car a trusted data source. By placing a node in the car, Streamr is also showcasing how a car can become a functioning part of a decentralized peer-to-peer network and enable data delivery at scale and low latency between vehicles and physical infrastructure.” – Streamr Network AG.
“DLTs suffer from complex infrastructure, high cost and low performance. This prohibits the entry into high performance markets like automotive. C-chain is a blockchain variant to solve those problems and therefore it is suitable for many new applications.” – Prof. Rudolf Bayer, Ph.D., Institut für Informatik, Technische Universität München.
“We are excited about this challenge because the connected car is a great example for the need of trust in IoT. With a couple of strong partners, we will showcase how trusted data from the car can help to establish trailblazing business-models in different sectors like insurance, energy
and fleet management.” – Stephan Noller, CEO at Ubirch.
It’s been another busy week as we announce that our lead research scientist, Marcin Abram, is overseas setting up our US office. To find out what Marcin has been up to over the last month, read his letter from America.
One of the many projects Marcin has been working on is a new case study to show how Fetch.AI’s unique platform can help to build a smart city. Using machine learning algorithms, autonomous agents can actively reduce congestion and optimise route planning and parking.
A vehicle recognition system implemented by Fetch.AI
If you’re looking for more brilliant weekend reading to get your teeth into, check out our CTO’s blog on the Decentralised Digital World of the Future. In this, Toby explains three ways in which Fetch.AI’s autonomous agents can navigate and make sense of their space in order to find what they want, or to deliver what they have.
BTCManager caught up with Toby to get a better understanding of how Fetch.AI can empower IoT devices with economic agency. To read the full interview click here.
In another exclusive interview, Coin Rivet spoke to Toby and Fetch.AI CEO Humayun to better understand the Fetch.AI vision for a smarter future. Read the interview here.
This week we caught up with principal software engineer Peter Bukva about what he is working on. Read all about how Peter is helping to incorporate the virtual machine into our ledger and how this will enable it to execute smart contracts.
On Friday, Fetch.AI’s Head of Cryptography, David Galindo, discussed the Future of Blockchain at the University of Southern California, as part of the Second International Symposium on Foundations and Applications of Blockchain 2019.
David discussing the Future of Blockchain at the University of Southern California
This week we also welcomed Chris Murray to the team. Chris’ extensive DevOps experience will help Fetch.AI to solve the challenges that building a new digital economy will bring. Find out more about Chris here.
A little over a month ago, Fetch.AI’s lead research scientist Marcin Abram moved from Cambridge in the UK to America, where he is setting up the company’s US office. In his first report, he explains how he is settling in.
Finding an office
We have chosen to locate the new office in New Haven in Connecticut. Its proximity to New York and Boston makes it an excellent base to travel anywhere along the east coast of the country. The prestigious Yale University is also in New Haven. We hope to collaborate with the university and create a research unit, focussing on applications of the Fetch.AI network in sectors such as the smart energy market and smart cities.
We have started working on a new case study to show how Fetch.AI’s unique platform can help to build a smart city. Using cheap cameras, we can deploy a system to monitor traffic conditions and the availability of parking spaces. It can also be used to monitor roads, junctions and potential congestion points: this data can be represented by agents in the Fetch.AI world and can deliver that value to other agents or people directly. This allows agents embedded in cars to optimise route planning. Additionally, it provides humans with useful, actionable information which is pushed to them to keep them informed on decisions. Individuals will be able to receive messages such as “it’s time to leave work now, traffic is building” or “it’ll take you half an hour to go home, but the other route will be quicker today”. This type of information will reduce traffic congestion, saving drivers both time and money.
Walking in the city, it is easy to forget the ocean is just a few miles away. The region is gorgeous: full of beautiful parks, hills and long, clean beaches.
Madison’s beach near New Haven
The month ahead
By opening a US office, we have opened ourselves up to a new market and we have already established many important scientific connections that will lead to new and exciting projects.
The plan for the next month is to clarify the scope of the projects we want to conduct in the US and to further strengthen our ties with new collaborators. We will start looking for people to hire in New Haven too. Our list of open positions include: a computer scientist, a machine learning engineer, a research scientist, an economist and a cryptographer. We are also always looking for people specialising in system design, game theory, reinforcement learning, multi-agent systems, decentralised protocols and consensus design. If this sounds like you, please send an email to firstname.lastname@example.org.
We are pleased to welcome Chris Murray to the Fetch.AI team.
Chris built his DevOps experience while working for companies in a range of different industries (Rakuten, General Electric, Repositive). His passion for self-improvement pushes him to learn and explore new ways to remove friction throughout the development and deployment pipeline. He is very excited to start work on solving the challenges that building a new digital economy will bring.
A Cambridge local, when away from his laptop, Chris stays active with bouldering, crossfit and by kayaking across Cambridgeshire. His fitness goals this year are to compete in longer distance triathlon races and to keep up with his 15-year-old son and girlfriend who is a running coach and fitness instructor.
If you are interested in being part of building Fetch.Ai’s decentralised future please visit our careers page.
Hi, my name is Peter. I’m a principal software engineer working in the Fetch.AI ledger team.
Currently I’m helping to incorporate the virtual machine into our ledger. This in turn enables our ledger to execute smart contracts and opens the path for us to implement our innovative Synergetic Contracts.
Over the past year my work has spanned across the core development of the ledger. I have been focussing on the design and implementation of the cryptographic features we need in our ledger using low level OpenSSL. There is more work to be done in this field and the team will continue to develop the necessary solutions. These include support for threshold cryptography features on the ledger and preparation for quantum-resistant cryptography. One aspect of this work also includes the transaction formats and how these are passed between various languages and frameworks. In recent months I have been working on how to enable easy interaction with our ledger across multiple languages. Our Python API is designed to be used for programmatic interaction with our ledger to enable creativity and automation. I have been driving the development of our mobile and desktop wallet apps from their inception. Wallets are essential for making our ledger testnet accessible for people in a classical sense – enabling monetary operations like transferring funds and checking balances. Maintaining the security of the wallets is essential. One of the key principles behind our design is the idea that it is the wallet, rather than the ledger, that dictates the signature scheme which is used.
Our first version of the wallets have been designed primarily for early adopters and we have placed an emphasis on making them accessible for experimentation. For example, the private key can be copied and pasted in order to allow easy interaction during the development and testing stage. You can check out our wallets for yourself by downloading them from the Apple App Store and Google Play Store.
We are extremely excited about the journey ahead and we have many plans for our wallet apps. These include preparing them for the launch of the mainnet by the end of the year and adding cool new features such as the creation and handling of multisig wallets and transactions.
Over the coming months we plan to demonstrate a number of interesting use cases. Following the successful recent launch of our wallets on mobile applications, we will continue to develop more advanced software such as biometrically enabled hardware wallets. These are currently in development and we are looking forward to sharing more details with you about these wallets in a tutorial on our community site soon.
It’s been an app-solutely brilliant week with the release of the Network Participation App on the Google Play Store. Now you can build, deploy and monitor autonomous economic agents using drag and drop and earn test FET tokens to use on the testnet.
Also released this week, a tutorial showing you how to create a working agent that can represent, advertise and deliver data on the testnet and our second benchmarking blog post which focuses on Single Lane Performance.
On Thursday, Fetch.AI CTO Toby Simpson participated in a live webinar with Outlier Ventures and Sovrin to demonstrate ANVIL: a decentralised, machine-readable solution that allows individuals and businesses to seamlessly prove their credentials within the infrastructure of Fetch.AI’s digital world. ANVIL helps us move a step closer to delivering the IoT economy of the future through the use of our Autonomous Economic Agents. Watch the full webinar below:
Friday saw the launch of a trading competition from Binance with 400,000 FET in prizes up for grabs.
We caught up with Diarmid a senior software engineer who is working on simulating scenarios showing how Fetch.AI can bring value to people. Read the full post here.
Also this week Fetch.AI CEO Humayun spoke to ICO Analytics about fundraising, the role of digital assets, competitors and outcomes from 2018 and the future of Fetch.AI. Read the full interview here.
Finally we are pleased to welcome this week machine learning enthusiast Jiří Vestfál and video and graphics content creator Jay Loe. Read more about how both Jiří and Jay will help bring Fetch.AI’s vision of a decentralised digital economy to life. If you’re passionate about programming and keen to get involved check out our careers page.
If there is one thing that science fiction has taught us, it’s that future digital worlds are terrifying places where human beings go to waste away in some kind of hedonistic virtual playground. But that is because we always see them from a human perspective: they are places we go, they may permit “magic” (in that you can do things that the real world disallows, such as float, fly and teleport), but they are essentially three dimensional, a metre is a metre and if we see digital entities they are robots, droids and other artificial constructs that inhabit that space along with us.
The Fetch.AI digital world is inside out — a complete opposite to “human first”. Instead, we present a world that is “machine first” in order to better serve humans who need big, complex problems solved, by optimising it so that those machines can get things done on our behalf. In this kind of space we can afford to bend reality much further: we do not need to restrict ourselves to the obvious magic and special powers, a metre does not have to be a metre and we can show a world on many more than three dimensions. This is a space that can be explored and navigated semantically, geographically, or by economic features.
For the digital inhabitants of the Fetch.AI world, things that are close are not close because they are near, they are close because they are useful. We call our digital inhabitants Autonomous Economic Agents, or just “agents” for short, and we have created a number of technological building blocks designed to deliver decentralised navigation. Here are just three ways in which agents can navigate and make sense of their space in order to find what they want or deliver what they have:
N-dimensional spatial exploration. The nodes on the Fetch.AI network are connected together on more than one layer. These layers provide an underlying geography of the network which allows agents to move from node to node to end up geographically where they wish to be. These layers also permit navigation by infrastructure decision points such as airports.
Semantic Teleportation and Vision. Fetch.AI uses learning to dynamically assign nodes on the network to handle specific subjects. The models that govern this are certified by the underlying permanent ledger. Agents can use this to position themselves on nodes that cover related or relevant subject areas.
Climbing or descending artificial chemical gradients. Similar to some path-finding algorithms, nodes are able to broadcast artificial “chemicals” that decay the further they get from their source, delivering gradients that can be climbed or descended to move towards or away from specific things. These are linked to semantic information, service availability (prediction markets, optimisation or planning functionality, etc.), agent population and more.
None of these navigational methods have to be used in isolation. Agents can combine them in different ways in order to effectively position themselves on the network to find the audience they need either to buy from or sell to. Collectively, we call the interface to this digital world the Open Economic Framework, or OEF for short. It is the OEF that provides the gateway through which agents connect to, explore, perform business and disconnect from the Fetch.AI environment. It is a unique space, that would look strange and alien to humans, but presents a highly optimised machine readable home for digital entities that adapts in real-time so that they are able to work effectively, alone or together.
In this article, we look at the three primary navigation methods provided to agents and touch on the exciting technologies that are used to deliver them.
1. N-dimensional spatial exploration
Care of, and thanks to, the awesome Threepwood at b3ta
Fetch.AI connects nodes together on several layers. Traditionally, nodes would connect to other nodes in a network based on network discovery, performance and other factors. There is no defined geography to this: whilst you can build an image of the network by looking at the various connections, it is not possible to walk north, or towards Canada, on such a structure in any meaningful way. Likewise, you can’t stand at one node and capture other nodes that sit in a cone bearing south from your location out to a range of 100km. When it comes to a network that is enabling agent discovery, these concepts become important. Many of the actions we take in life relate to position and direction and Fetch’s n-dimensional spatial organisation makes this possible.Fetch’s node connections are layered. As well as the typical network based connections, there are also spatial ones based on declared location. It is possible to walk around the network in order to find nodes that are closest to a specific location. This means that if you’re in London and you wish to find agents that are in New York, you can take the minimum number of hops on a decentralised peer-to-peer network to get there. It also means that if you’re setting up a node on the network, you’re able to position yourself where there is agent demand: perhaps a second node in London, or one that focusses on the Heathrow Airport area, encouraging agents that represent value (or desire it) in that area to move in that direction.
A Fetch.AI network of black and white agents navigating the UK’s cities (red) and airports (yellow), without revealing their own location or their goal to the infrastructure nodes. Solid lines are the agent’s connectivity, dashed lines are their “secret” goal location: this is real, it works, and it’s a screenshot from running code.
Such a method of connection is dangerous if it is not done correctly. Indeed, if it is the primary method of network connection then it is easy to “island” vast areas of the network — cutting them out and isolating them without the individuals realising it. This is far harder in an n-dimensional world as there are several entirely independent layers of connection for entirely different purposes. In the case of Fetch.AI, we define network-based connections, geographic ones and infrastructure ones but there is absolutely no reason why there might not be many more of these, each providing a filter for movement to make one’s journey even more efficient.
2. Semantic Teleportation and Vision
Buckle up, folks, this is going to be one hell of a ride!
Fetch.AI offers agents the the opportunity to explore and view the world semantically, or, to put it another way, by content. There is a family of AI technologies which vectorises data: takes input data, such as words, text and images, and turns it into a mathematical set of coordinates. The result is referred to as an embedding, and by looking into this new reduced space, things that are close together tend to be related. One of the common examples of this is document-to-vector (doc2vec). In this case, pieces of text can be fed into the system and it can be seen which documents are similar, or related, by simply looking at the results. In this way, one can determine similarity and relationship without knowing the details, i.e., that two documents are semantically similar, but not having to know that the category is animals, or history. This is an extraordinary powerful concept as it requires no prior subject knowledge in order to find related material: you take what you’re interested in, vectorise, gather everything within range and voila, you have the things that are closely matched. In the same way, adapted variants of things like code2vec can be used in architectures like Fetch.AI in order to find similar smart contracts. You can, for example, take a token contract and find all similar token contracts on the ledger¹.
This technology is not restricted to just text. It can be used for images, too. You can take an image of a number, feed it into such a system and figure out what it is just by where it positions itself: “yes, this is probably a 4 but it does look a touch like a 9.”.
Well, that’s great, I can pop an image of a number in and find out what it is likely to be, but also which numbers I am close to in style and shape². But that isn’t all it does. This kind of technology means that instead of having a deep neural network that can tell you if an image is a dog, you can have a system that positions your drawing, picture or model of a mystery animal with animals that are similar. You can then capture a circle around your position to figure out what your image is likely to be and what it is similar to. So that poorly drawn zebra? Yes, it’s probably a zebra, but it is certainly very much like a horse, donkey, pony, a bit like a giraffe and so on. Or, if you start at zebra and look towards, say, monkey, what animals do you intersect? Effectively, we’ve built a really cool animal approximation machine that’s spectacularly good at telling you what yours is and when it misses, the ones close by will almost certainly be correct. Now extend this further: do the same thing with subject areas. Transportation. Hospitality. Healthcare. Interested in the markets that might be relevant to you? Look around you in semantic space and see. Interested in the business opportunities from transportation towards healthcare? Stand at one point, look towards another, see what you intersect. There is absolutely no reason why such a system needs to be restricted to numbers, animals or any other specific subject: you can pump it with information and end up in a reduced dimensionality world where you are near to things that are similar to you. And this is what we call our semantic dimension.
In a decentralised world, though, there’s a catch. If you distribute the semantic data across the entire network, you have a lot of data to synchronise. That’s all traffic and in the meanwhile, we’ve got transactional data, agents moving and doing their stuff and all the network management stuff going on that’s using the vast majority of these pipes for network critical operations. Synchronising a giant neural network and all its associated gubbins is going to cost. On Fetch.AI, we do this by assigning captured areas of the vectorised space to individual OEF nodes. Animals, and associated subjects, may be on OEF node X and transportation on node Y. When you generate your embedding, the OEF’s shared advanced semantic index will tell you which nodes you should be talking to: probably node X, W and T. You can then connect directly to these in order to capture the agents you’re interested in. This is an entirely dynamic, constantly shifting process, as the learning models that do this update continuously as the requirements of the network’s users change. This process of establishing where you sit in semantic space and the leaping to the node you should be at is a concept we call Semantic Teleportation. It can, of course, be combined with other network exploration methods to refine a search further (e.g., in the area of Paris, with the subject area of healthcare).
Fetch.AI performs this using partially or fully populated data models. It means that if you’re looking for a certain kind of weather data, but you’re not sure how it will have been advertised, you will be able to teleport yourself directly to a field full of people who are most likely to be providing information that is similar to you. As an advertiser of data, with value to deliver, this is also particularly exciting because you can create this position in semantic space based on the actual data you are delivering and not just the model that describes it. It also affords agents the ability to explore by subject: teleport to an approximate area and stroll around looking for other opportunities that might be relevant. But here’s the kicker: you’re doing this without revealing the data itself. Now clearly, if the OEF (i.e., the actual node itself) is performing this work and calculating the semantic position, then it is doing so with the data and this, particularly for a naughty node, is a potential privacy risk. Whilst the node can provide this service, in most scenarios, we see the agent itself performing this calculation to position itself in semantic space and delivering what we call “the dimensional reduction” directly to the node: i.e., “put me here, but I won’t tell you why”.
3. Artificial Chemical Gradients
Little program I knocked out the other night to show single-layer diffuse navigation. Drop us a line (info at fetch.ai, subject “Toby’s program thingie”) if you have a Mac and fancy a copy — it’s a small, native C++/Objective C app, no warranty, no polish, your own risk, blah blah blah…
In computer games, one of the many ways in which we achieve path-finding (how to find the best way from A to B in a complex environment) is through a mechanism where the desired destination acts as an emitter of a large number. This is like a tap turned on with water pouring out of it: the further away you get from the tap, the less water there is. You can see this literally in Minecraft, if you’re curious. In the meanwhile, some of the surrounding areas are more porous than others and absorb the water faster, whereas others do not and the water goes further. If you do this with numbers, you start with a big one and as you move away from the tap, the lower the number gets. In its simplest form, this is implemented by dividing the world into a grid, and each square calculates its current number by looking at surrounding squares. In the absence of something topping them up, the numbers decay to zero, but somewhere (either as a stationary or moving target) there are one or more emitters that have a nice large number attached to them pouring more into the network.
The net result of this simple mechanism, when it is gradually iterated out, is that you if you start anywhere in the grid and move towards an increasing number, then no matter what you do, each step you take puts you closer to where you wish to be — guaranteed. The larger the increase in number, the better the route you’re taking and by looking at adjacent grid squares you can figure out how secure, or stable, your route is: the thinner the path, the more likely a blockage is. It also works in real-time, too, because the emitters are attached to the destination and each square can change its permeability depending on whether it is suddenly passable or not. By treating each grid square as an autonomous cell (in a cellular automata type way), the whole system self-corrects and scales wonderfully. With these recalculations taking place over time, you get some amazing outwards behaviour, especially if, say, you’re trying to get a hoard of roman soldiers or a herd of stampeding buffalo across a bridge.
This kind of path-finding is not restricted to grids, of course, as cells do not need to be connected in any specific way. They can, for example, look more like the road network between population centres. And it is this where it gets interesting: if we treat each node on the peer-to-peer network as a cell, and give it some rules on how to update “its number” in the route-finding system, you get a novel way of navigating the network: you can walk towards or away from what you want, even if what you want is a moving target and cells vanish, appear or change their permeability with little or no notice.
So why do we refer to them as chemical gradients? One of the fields that we have experience with is using an artificial biochemistry as an analogue computer. Specifically, such technologies have been used to create interesting, consistent and believable behaviour in artificial characters in gaming environments but also agents for other purposes that are able to adapt to surprises. The outward appearance of such systems is a lot like chemical messaging in nature, and coupled with other metaphors from biology, such as reactions along with emitters and receptors to interface to the outside world, a powerful environment for intelligence is created. It is this kind of environment that we wish to be able to provide to our agents.
For gaming on the Fetch.AI network this opens a bunch of interesting doors, as it makes “hide-and-seek”, with staking, really exciting (armed with some cool smart contracts, it’ll cost you to be found, and reward you handsomely to either find, or be found last). But games³ are not why it exists: it’s there to provide a third way of navigating the network, that can be combined with n-dimensional spatial exploration and semantic teleportation to make even the most complex journey look easy.
Agents are not restricted exploring the digital world in these ways. Indeed, they do not even have to treat it as a world at all: they can merely connect, advertise what they have or want, and wait for suitors to be brought to them. For those that wish to actively explore the space around them looking for new markets and opportunities, then Fetch.AI provides interaction methods for just that. These, coupled with the ability to see, search and filter by content with no requirement for the network to have prior knowledge of that content is particularly interesting.
This is a new world. It’s one that provides machines with abilities that only movies can convey upon us mere mortals: imagine if you could teleport yourself magically to a room full of just the restaurants you like that have a perfect table available? Imagine if all you had to do was think of approximately what you wanted, and you’d suddenly be surrounded by relevant possibilities. If you could hop, skip and jump towards your destination, seeing interesting and relevant things along the way. Imagine if you could smell the optimal holiday and walk blindfolded right at it, touching car hire, hotel transfers and the perfect flights just by reaching out. And it is this, all of this, that Fetch.AI gives to its inhabitants: autonomous economic agents that couldn’t live in a more optimal world if you spent a month of Sundays trying to perfect the dream.
 — I hate to pop spoilers in, but you may find the next couple of months illuminating with regards to such things…
 — There are many applications in which this is used: if a card number or some other optical recognition fails (many of these types of numbers have magic check digits to establish errors), then such systems can be used to rapidly try the best-guess approach to auto-correction.
 — I’d be delighted to discuss Fetch.AI’s chemical gradients coupled with n-dimensional spatial organisation as a gaming environment, but there is a condition attached: it has to involve a bottle (or two) of decent red wine.
Hi, I’m Diarmid. I’m a senior software engineer and I am currently working on simulating scenarios showing how Fetch.AI brings value to people.
The goal is to understand in detail how Fetch.AI Autonomous Economic Agents will behave in the real world so we can build our technology to support this behaviour. In addition, we are able to produce videos of the simulations to help the wider community understand the scope of our ambitions.
In the simulation below we are looking at the problem of charging electric cars on long journeys. People usually leave their electric cars to charge overnight, but if they need to recharge en route, drivers can face a long wait as it can take an hour or more to fully recharge their vehicle.
The scenario on the left with the orange cars represents common human behaviour. The electric cars in this part of the demo travel until they run low on power. At this point they check their Sat Nav system and look for charging stations that are within range. They pick the one which is the shortest detour from their main route and go there.
The difficulty for the human drivers is that when they arrive they may find that lots of other drivers have had the same idea and a long queue has already formed. A queue at an electric charging station is much more problematic than one at a traditional petrol station because you may have to wait for hours just to get to the front of the queue.
The scenario on the right-hand side of the video with the blue cars represents a Fetch.AI-enhanced scenario. The cars contain a Fetch.AI agent which is monitoring the car battery’s power level and is able to communicate with other agents on the Fetch.AI network. The charging stations also have a Fetch.AI agent monitoring the current waiting time for cars at the station.
The car’s agent monitors the battery and when it runs low on charge it searches for charging stations that are within range (so far, the same as the human behaviour). The car’s agent then seeks information about the current wait time at nearby stations from each charging station’s agent. The car’s agent then calculates the total detour time by adding up the time taken to get to the station, the wait time and the time to drive from the station to the destination. It can then choose the station offering the shortest detour overall (after factoring in the predicted time that would be spent waiting at a charging station).
As the car is on its way to the charging station, it continually monitors the wait times of the local stations and if the “shortest detour” station stops being the shortest, it begins the process again.
This inter-agent communication drastically reduces wait times at the charging stations. It also allows charging stations off the main roads to attract more customers.
There are many simplifications in this simulation. The most obvious is that, in reality, if a human arrived at a charging station and saw a 20-hour wait, they would go looking for an alternative station. However, we expect the kind of benefits we see on our simulation to carry across to the real world.
In our simulation, cars that didn’t queue at all completed the journey in about 8 hours and 30 minutes. The cars without the Fetch.AI agents took an average of 13 hours and 30 minutes. Cars with Fetch.AI agents took approximately 9 hours and 30 minutes — a 30% reduction in the time it took cars that weren’t using Fetch.AI’s technology.
This use case is also highly relevant when demonstrating the potential of machine verification. In a recent article, Fetch.AI’s CTO Toby Simpson outlined the importance of being able to prove your identity in today’s digital world. Machine verification is already prevalent in society and it will be adopted increasingly widely for everyday actions in the future. To take one example using electric vehicles, charging points owned by particular car manufacturers will want to know they can trust the vehicle they are being plugged into.
Fetch.AI recognises the importance of collaboration to deliver the IoT economy of the future. We are currently working with Outlier Ventures and Sovrin to develop ANVIL. The software allows individuals to seamlessly prove their identity to trusted representatives. This is an exciting step forward and we’ll be showcasing the technology and how it will function on the Fetch.AI network during a live webinar demonstration tomorrow (28 March). To find out more, please sign up here.
Fetch.AI are thrilled to welcome experienced software engineer and machine learning enthusiast Jiří Vestfál to the team.
Jiří is passionate about artificial intelligence and enjoys playing with evolutionary algorithms in an attempt to create artificial life or predict bitcoin prices. He has gained experience in many fields including image and data processing, evolutionary algorithms and low-level protocols. Before joining Fetch.AI Jiří honed his C++ skills working on the back end for Domino Printing’s industrial printers.
In his spare time Jiří uses DSLR cameras to take pictures that are invisible to the naked eye – his photography fields of interest include astro, macro, infrared or night photography with extreme long exposures.
Jiří is excited to start working on algorithms that enable the visualisation of high-dimensional datasets and language processing that will bring Fetch.AI’s vision of a decentralised digital economy to life.
If you would like to get involved please visit our careers page.