What are you working on? Checking in with Nathan Hutton

Hi! I’m Nathan. I’m a senior software engineer, and I have been working on the Fetch.AI ledger for the last 18 months.

As you might know, the ledger confirms transactions between two or more parties in a decentralised manner. This involves some considerable design challenges. Two of the aspects I have been working to resolve are the networking capability and storage.

Networking is an interesting problem because the Fetch.AI framework allows clients or peers (users and miners) to make RPC invocations to your node at any time. As a result, it is necessary to write the code very carefully to avoid race conditions or memory errors. Other than just making and receiving RPC from node to node, there are also higher level networking requirements. Miners need to self-organise into a structure that avoids bottlenecks and minimises latency when transmitting information. Two examples of such a transfer are block and transaction synchronisation.

These issues are solved in different ways: when a miner creates a block, to minimise the time until it is seen by all miners, a gossip protocol is used to propagate it through the network. Transaction synchronisation is markedly different. Here, each miner maintains a small ‘transaction sync pool’ of recently seen transactions. Peers of this miner can then make requests of their own to ‘pull’ transactions they haven’t seen from this pool. This allows miners, on both sides of the call, control of what is potentially a fairly expensive operation.

With regards to the storage, this again presents its own challenges. Fetch.AI’s novel solution to achieve high transaction throughput at scale is to shard the ‘world state’ into a number of resource lanes, allowing miners to increase or decrease the number of shards at runtime to respond to changing system demands and economic incentives.

For those who are unfamiliar with this term, the world state in the cryptocurrency space refers to the conceptual ledger that all nodes agree on. Executing a valid transaction will necessarily alter this world state — in the case of a token transfer this would decrement the tokens at one address in the world state, and increment it at another address in the world state.

The consequence of this sharding scheme is that in addition to the usual challenges and stipulations one might associate with database design (mainly access times for items as the database grows, but also importantly on-disk size), you have to support splitting or merging the database. Moreover, the database must support reverting back to a specific block, which may have a different lane count. By reverting, I mean reverting the world state (all shards) to a specific commit. Therefore, if the miner switches from one fork to another, the database will revert to the state immediately before the fork. This process will enable the miner to execute the blocks from this branching point up to the head of the new fork.

For those interested in learning more about Fetch.AI’s transaction lane concept, I recommend checking out our yellow paper and to keep an eye out for my next blog post.

Fetch.AI Public Test Network: Foundation Release, April 2019

  • Public testnet released on-time: now everyone, not just those who took part in the token sale, can access, use, experiment with and develop on the Fetch.AI decentralised network
  • Block explorer and status page: easy-to-use web based block explorer for looking up transactions, contracts, etc., and a status page to view what’s happening with each testnet component
  • Decentralised search for agents: agents can now search across multiple nodes to find other agents to work with using Python, C++ or other languages
  • Smart contract language playground: learn to develop in Etch, Fetch.AI’s smart contract language, inside a simple, web-based interface
  • Foundations laid for future releases: the testnet foundation release enables the developer community to engage in smart contract and decentralised computing development

Today, we’ve published the latest smart contract ready version of the Fetch.AI Smart Ledger, we’ve released a block explorer, a testnet status page, an amazing decentralised search facility as part of the magic that makes up the Fetch.AI digital world and, to top it off, a super-cool web-based “write, click and run” interface to the Fetch.AI Virtual Machine (VM) and its unique Etch language. This is our foundation release, it’s the first time that all the key component parts are all in the same place at the same time. It enables rapid iteration, as we can now release the updates we’ve been working on much faster. Most importantly of all, though, this brings you into the picture: our supporters, the developer community and our token holders. Now, alongside the drag-and-drop make your own agent Network Participation App (NPA), you can explore the network’s activity, play about with code, monitor the status and much, much more.

A lot has happened today so let’s break it down and talk about it in a touch more detail. Firstly, let’s look at the utility functionality: the testnet status and the block explorer. Most of us in this space are familiar with block explorers: a website where you can watch what’s happening on the blockchain, look up contracts, transactions and more. We now have one for our native testnet, and you can access it any time you wish. Of course, this is a testnet, so there will be times when it is broken. Perhaps we’ll break it, perhaps you’ll break it, but either way, a status page is vital for self-service information on what’s up. Developers, of course, will rely on such services to ensure it’s the network that’s having problems as opposed to their lovingly crafted code!

The service status page shows another point: there isn’t just one test network, there are three. And there are two primary reasons for this:

  1. It allows us to expose the super-new version alongside the slightly older version that’s well supported by our mobile wallet and NPA app.
  2. It allows us to release new OEF functionality (such as increased dimensionality search) without breaking anything else — and those releases are going to happen rapidly.

In the first half of May, we’ll gradually collapse these networks into one. As we all collectively use the testnet, run it under load and see what happens as the nodes are expanded, there will be a refinement process. During this time, we’ll move the whole primary network across to the newer one, which we call the X2 network. The X2 network supports full smart contracts, synergetic computing and has an updated transaction format. As part of this transition, we’ll simultaneously be pushing updates to our iOS and Android apps.

Decentralised search for agents

Then there’s the OEF: the Open Economic Framework. This is our Digital World, the interface through which agents can find each other and get work done. It’s super important, and we have great plans for it. In this release, we demonstrate a geographic dimension to the world and a true decentralised search facility. You can use Python and C++ to easily create agents that use this system, either through our SDKs now (although some features are missing in the C++ API until early May), or you can attach to the APIs in the OEF yourself immediately. This release lays the foundations for the other parts of decentralised search: using a concept of “data access providers” (or DAPs), nodes are able to quickly evaluate which parts of the network are irrelevant, so that they assess only the agents or locations that are important.

Crucially, the architecture scales well locally, as well as across the network. All the components involved, the OEFCore, the search component, the DAPs and the ledger all talk to each other over TCP/IP. This means that they can be on the same, or different machines. A large, full, OEF node on the Fetch.AI network may consist of just one, or many individual computers (one could construct a node out of a whole stack of Raspberry Pis, for example).

The Etch Virtual Machine

We’re particularly proud to deliver this version of our virtual machine. This allows a huge range of artificial intelligence computing to be done inside smart contracts, making them truly smart. AI, and in particular, machine learning, can be used to deliver useful, actionable predictions that enable agents to work on the basis of understanding what will be, rather than what has been. It also enables decentralised autonomous organisations to trade in a way that previously was simply not practical. Up until now we’ve been releasing parts of the VM, but this is the first version which truly allows for these innovations to be built and deployed and we have some incredibly exciting news about the VM in the coming days.

Etch: a playground

Given the importance of developing in the Etch VM and its impact on the smart contracts of the future, we’ve built a beta web-based interface that lets you easily write, test and run Etch. We’re working on a grander version of this with tutorials and reference material, but we decided that it was worth pushing an initial version of this out today: this was too much fun to notrelease, especially as we’ve had so much fun developing things like the Mandelbrot Set alongside the token smart contract and more. So we’re sharing our Etch playground now. Starting Monday 13th May, we’re going to run some competitions. All participants will receive ERC-20 tokens, with the winners earning more. Releasing our playground now lets you explore, and lets us swap some of the cool things we’ve been playing with on Telegram and elsewhere. Plus, we would love your feedback!

Etch generating the Mandelbrot Set. This is the tip of the iceberg when it comes to computation that can be done on the Fetch.AI ledger — large-scale AI applications can deliver incredible knowledge and prediction services to the network’s users.

And this is just the beginning

We are proud to have over-delivered on this milestone. In the next two weeks, you’ll see Etch documentation, more OEF deliveries and a gradual merging of the testnets into one smart contract, synergetic computing capable network. This foundation release places all the component parts together in a way that they can be easily used for commercial applications and we’re looking forward to sharing news on that front shortly.

How’s that for a Tuesday?

Fetch.AI Community Newsletter (22–26 April)


Here is Fetch.AI’s community newsletter covering the period from the 22–26 April. Stay up to date by following us on Twitter.

Upcoming events

  • 1 May, Cambridge. PyData 7th Cambridge Meetup. Fetch.AI Lead Research Scientist Marcin Abram will give a talk on how to combine blockchain and AI.
  • 6–7 May, Paris. Tokenomics 2019 Conference. Marcin Abram and Daniel Honerkamp will be presenting a paper co-authored by members of the Fetch.AI research team, titled “Democratising blockchain: A minimal agency consensus model”.
  • 13–15 May, New York. Consensus 2019. CTO Toby Simpson will be part of a panel discussing Exchange Growth Tactics.

Trusted IoT Alliance’s Smart E-Mobility Pace Tour:

Technology update

  • We have been finalising the ledger and preparing the next generation of the Open Economic Framework (OEF) for the release of the public testnet.
  • The public testnet is on track to be released at the end of this month. See the full 2019 technical roadmap.
  • Visit our GitHub Repository to view, download and use all of our code, including newer versions of the ledger and OEF yet to be used on the public testnet!

Community news

Binance Incubation AMA
CTO Toby Simpson was in Berlin this week taking part in an AMA with Binance Labs’ incubation program as a mentor.

Toby at Binance’s Berlin Incubation AMA

TIoTA Pace Tour — Zurich
IBM’s Zurich HQ was the next stop on the Trusted IoT Alliance’s Smart E-Mobility Challenge this week. Partners Bosch, Riddle&Code and Streamr explored how a decentralised blockchain-powered real-time data marketplace can optimise the road network and increase safety. See the latest updates on TIoTA’s Challenge Wall.

TIoTA’s Challenge Wall

Bitmax competition
BitMax launched a competition to win thousands of FET tokens. Users who correctly predict price movement for five tokens including FET will receive equal share of reward worth 40,000 USDT. See full details here.

Rebuild Conference, Toronto
On Tuesday lead research scientist Marcin Abram gave a presentation about token engineering at the Rebuild Conference on technology in Toronto. Marcin discussed the blockchain trilemma and how we can find a correct balance of security, decentralisation and scalability.

Marcin Abram presenting at the Rebuild Conference in Toronto

Thank you for reading our community newsletter. Join the conversation with the rest of our community on Telegram.

We hope you have a great weekend.

What are you working on? Checking in with Chris Atkin

Hi, I’m Chris, the senior marketing executive at Fetch.AI. Since I joined Fetch.AI last summer, the team has grown dramatically. Throughout this exciting time, my role has focussed on engaging with the community and sharing company updates on our website and across our social channels. In the process, the number of followers we have on Twitter and LinkedIn has increased fourfold and we now have a combined total of more than 20,000 members of our English and Chinese Telegram channels.

We’re delivering on our technical roadmap every day and this provides us with a rich seam of information to mine. In recent months we’ve launched the FET Wallet app and the Network Participation App on Google Play Store and the Apple App Store. We also recently announced the development of ANVIL, an interaction framework developed by Outlier Ventures that combines our technology with that of Sovrin to allow individuals and machines to verify credentials in real-time.

The community’s passion for Fetch.AI is self-evident and it’s great to be involved with a project that so many people care about and support. We understand the frustration some of you feel when we are unable to share updates with you immediately. “Soon” is never soon enough. Negotiations regarding collaborations and partnerships must be conducted in private and unfortunately take time, but it is crucial we get them right. Rest assured, negotiations are continuously taking place and we are looking forward to announcing partnership news in the coming weeks.

One of my main tasks is to promote the content generated by our world leading team. How we do this differs according to the platform we are using. For example, the average Telegram user has a different profile to the typical audience member on LinkedIn. Our broad church of supporters includes investors, corporate partners, academics and crypto enthusiasts, to name but a few groups. In addition, we encourage developers to explore the Fetch.AI network of Autonomous Economic Agents and we have recently begun to share guides on exactly how to do that.

In order to keep the community up to date, I regularly share interviews with members of the team including CEO Humayun Sheikh and CTO Toby Simpson. I also publicise information such as the conferences the team have attended, the names of exchange platforms where FET tokens are newly listed and I oversee the production of blog posts and Medium articles where we profile the groundbreaking work of members of our team.

As a marketer, I wouldn’t be doing my job if I didn’t encourage everyone reading this to tell their friends about Fetch.AI’s unique technology. You can find out more information on our website and keep up to date with our progress by following us on the platforms below:


Furthermore, if you’d like to work with us, please visit our careers page.

Fetch.AI weekly newsletter #032 – New listings, meetups and the press year in review

A team of commodity trading experts joined our advisory board this week. Abe Ulusal, Phillip Price and Jonathan Fish bring a wealth of experience that will help us to introduce greater efficiency and improved transparency across a range of sectors.

A new listing was announced with FET tokens now listed on the popular South Korean exchange platform Korbit.

Also this week, the 7th PyData Cambridge Meetup was announced. The event is sponsored by a number of companies including Fetch.AI. Our own Lead Research Scientist Marcin will be returning briefly from the US, to speak about how to combine blockchain and AI. Join us on 01 May, 19:00 at Raspberry Pi HQ, RSVP here.

We have come a long way since our public launch last year. Our innovative technology and successful public sale have seen us featured in Forbes, The Economist and The Telegraph. We took a look back this week on some of the many publications that mentioned us in the last year.

Finally, we are pleased to welcome Wojciech Kaluza to the development team. Wojciech’s passion for software engineering and continuous improvement at all stages of the product lifecycle, will help to support our transition from testnet to mainnet at the end of this year.

A year in the media

It all started in Zurich in March 2018 with this video and our technical introduction paper. Since then, we’ve featured in 530 articles, spoken at 28 conferences and hosted six meetups around the world. In this time the number of followers we have on Twitter has grown to more than 7,000, and our English and Chinese Telegram channels have a combined total of more than 20,000 people.

It has been wonderful to see the Fetch.AI community grow. International media coverage has outlined our ambitions to create an economic Internet: where digital entities (which we call Autonomous Economic Agents) that represent all the component parts of the economy, can get things done for you, without human intervention. This endless population of agents can solve complex problems on your behalf, delivering real solutions directly to you whilst protecting your privacy. It’s all about making your life easier – taking today’s complex digital world and making it work for you, not against you. As we mark a year since the company came out of stealth mode, we wanted to reflect on the progress we have made and the media attention we have received in the process. Our technology has the potential to revolutionise a wide range of industries, and, as you’d expect, the coverage reflects this. We’ve had coverage in areas such as machine learning, the wider artificial intelligence space, data security, privacy, consensus, decentralised networks and commercial applications such as travel, hospitality, healthcare and much, much more.

Early on, we met with the team at The Economist to introduce them to the concept of the Fetch.AI network. We explained how our technology will transform the data marketplace and simplify the process of exchanging data.

In May, TechCrunch was one of a number of publications that reported we had joined the Mobility Open Blockchain Initiative (MOBI). Alongside BMW, Ford, Accenture and many others, we are committed to making travel safer and more accessible using blockchain technology.

Our interview with The Telegraph in August looked at our ambition to democratise AI. The interview with chief executive Humayun Sheikh covered everything from potential use cases to the need to decentralise technology to increase global implementation.

In December, Business Weekly, the Cambridge business publication, announced our founding membership of Blockchain for Europe, alongside EMURGO, NEM, and Ripple. We’re proud to work with these organisations to help shape European policy on emerging technology.

At the turn of the year, we featured in The Guardian where our decentralised approach to data privacy was compared and contrasted with the present dominance of the technology giants. Currently, we are expected to surrender our data for the benefit of major corporations, but Fetch.AI empowers individuals to own their data so they have the ability to decide whether they want to share it, and if so, in what way.    

We are developing our technology with the machine-to-machine economy in mind. Our feature in IoT Now explained how deploying Fetch.AI autonomous agents in machines would help machines to communicate, enabling them to operate more effectively and efficiently. In a separate article, we also discussed how the utilisation of Fetch.AI agents would radically improve the rail network.

At the start of February, Blockchain News announced we had joined the Trusted IoT Alliance. TIoTA was founded to support the creation of a secure, scalable and interoperable IoT ecosystem and includes major corporations such as Bosch, Cisco and Siemens among its members.

We are extremely proud to have been featured in Forbes twice over the past six months. As well as being included in an article about our work in the convergence of blockchain and AI, the excitement generated by our token sale on Binance Launchpad was also reported by the publication. The coverage demonstrated the growing global interest in our work and helped to extend awareness of our technology across Asia.

During our public token sale on 25 February we raised $6m in just 22 seconds, with orders pledged by nearly 20,000 people. The event was reported by several publications, including Coin Telegraph. Following the success of the sale, the team has been focussed on delivering the technology milestones listed in our roadmap.  

2019 will be the year that Fetch.AI’s population of autonomous agents grows, and the community will see the delivery of real applications that you can touch, use and engage with. Our FET wallet and Network Participation App are already live on Google Play Store and the App Store. Last month, our breakthrough solution to the blockchain trilemma was analysed in Blockchain News, while Computing discussed our new decentralised consensus protocol.

We continue to add to our world leading team of experts as we hire the best minds in cryptography, machine learning and AI. This is just the beginning. Stay tuned for further updates as we implement our innovative technology.

Wojciech Kaluza joins Fetch.AI as a software engineer

Fetch.AI are excited to welcome Wojciech to the developer team.

Originally from Poland, Wojciech came to the UK to study Chemistry at Oxford, and later decided to pursue his passion for software engineering. He gained experience while working for Hewlett Packard Enterprise and DisplayLink on projects ranging from Big Data and Enterprise Search to ultra low-latency display drivers.

In his spare time he perennially fails to finish a book on the Polish language. When not at the computer, he can be found experimenting in the kitchen, roasting coffee and making pickles.

Wojciech’s fervor for continuous improvement at all stages of the product lifecycle will support Fetch.AI’s transition from testnet to mainnet at the end of 2019. He is particularly excited to contribute to cutting-edge smart ledger technology that will one day underpin the future digital economy.

If you are passionate about programming and would like to explore opportunities within the Fetch.AI team, visit our careers page.

Fetch.AI welcomes a team of expert commodity trading advisors

Abe Ulusal, Phillip Price and Jonathan Fish join Fetch.AI’s advisory board

Abe Ulusal is the former chairman of the London Metal Exchange Steel Committee and has a strong understanding of both physical and paper markets, having worked as an analyst, physical trader, and risk manager in industrial metals for more than 17 years. Abe specialises in new business and international project development in the industrial commodities sector. He is an executive director at Mitsui Bussan Commodities Limited, where he initiated and developed the company’s ferrous risk management business. Mitsui Bussan Commodities is a member of the London Metal Exchange and provides pricing and risk management services in base metals, steel and iron ore on a global basis.

Phillip Price is deputy chairman of the London Metal Exchange Steel Trading Committee and works closely with other exchanges, including SGX, CME Group and Nasdaq, to deliver solutions for the iron and steel industry. He is a founding partner of Ferrometrics LLC, a company that specialises in developing liquidity in nascent derivatives markets for customers seeking to enhance their business. Prior to Ferrometrics, Phillip was Head of Risk Management & Derivatives Trading for Stemcor, one of the largest steel trading firms in the world. Here, he designed a wide variety of derivatives-backed strategies before, during and after the group’s restructuring. Previously, Phillip was Head of Steel for London Metal Exchange and Steel Editor for Metal Bulletin.

Jonathan Fish has spent his professional career trading and marketing a wide variety of commodities. Jonathan began at EDF Trading, a large European energy trader, focusing on renewable energy credit trading and later on, carbon emissions. In 2006 he moved to Goldman Sachs where he broadened the products he focused on, managing risk positions in a wide variety of exchange traded products including uranium, carbon, iron ore, coal, fx and interest rate products. In 2015 Jonathan was part of the founding team that established Javelin Commodities Trading, a highly successful coal trading house, where he was responsible for a variety of trading activities including SGX iron ore and coking coal.

Fetch.AI chief executive Humayun Sheikh said, “We are very excited that Abe, Phillip and Jonathan will be joining Fetch.AI as advisors. Our ambition is to commoditise several products which trade very inefficiently today. Fetch.AI’s decentralised network utilises machine learning and AI to enable greater efficiency and better risk management. It will also make way for improved transparency in several sectors like energy, hospitality, travel and transport. Abe, Phillip and Jonathan’s expertise will help us realise these ambitions.”

About Fetch.AI

Fetch.AI delivers a groundbreaking economic internet that enables emergent solutions to complex problems. 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.

Telegram: https://t.me/fetch_ai
Twitter: @fetch_ai

Fetch.AI weekly newsletter #031 – AI, blockchain and automobiles

This week saw the start of the European PACE Tour as part of Trusted IoT Challenges Worldwide. The series of challenges aims to define the emerging blockchain for the IoT space while creating roadmaps, standards and guidelines for implementation.

Autonomous agents met a sleek set of wheels at TIoTA’s Smart E-Mobility Challenge hackathon in Stuttgart. Fetch.AI’s head of business development Maria Minaricova and lead software engineer Joshua Croft worked alongside partners Share&ChargeStreamrRiddle&Code and Bosch to develop new technologies that will enable autonomy in the machine-to-machine ecosystem.

As part of this hackathon we demonstrated how autonomous agents can transact independently of human intervention to facilitate electric car charging, parking and route optimisation.

Josh and Maria with TIoTA and our partners

“TIoTA’s Smart E-Mobility challenge is the first step towards bringing awareness to the new digital economy for mobility.” Maria said. Keep up to date with the latest news on the E-Mobility Challenge.

World-leading artificial intelligence researcher and Head of Department of Computer Science at the University of Oxford, Professor Michael Wooldridge, joined us as an advisor. Michael’s multi-agent systems expertise will support our efforts to deliver the economic internet of the future.

Head of research Jonathan Ward gave a presentation on the wisdom of digital crowds and how blockchains can be used to deploy AIs in the real world at the World Summit AI this week.

In the second part of CoinRivet’s two-part interview, Fetch.AI CEO Humayun Sheikh and CTO Toby Simpson discuss how Binance’s due diligence enables credible projects and is bringing confidence back to the crypto space.

Earlier in April Toby spoke to 8BTC about the enormous potential of the machine economy. Read the full interview here.

Finally, we released a guide on how to build a minimum viable token generation contract using Etch, the language used to generate bytecode for the Fetch.AI Virtual Machine.

What are you working on? Checking in with Patrick Motylinski

Hi, I’m Patrick. I’m a research scientist at Fetch.AI. My background is in high energy physics phenomenology, and after working as an academic researcher for several years, I moved into the colourful world of tech start-ups.

At Fetch.AI, I have been working across a range of subjects, conducting research into consensus protocols, studying signature schemes in decentralised and distributed networks, and more recently, working on a data mining project with one of my colleagues, Daniel Honerkamp. In this piece I will be focusing on the latter.

The starting point for the data mining project is the GPS Trajectory dataset, collected by Microsoft Research Asia for the GeoLife project. The most recent dataset consists of trajectories made up of GPS coordinates, collected by 182 mobile phone users in the period spanning from April 2007 to August 2012. It consists of sequences of time-stamped GPS points. There are 17,621 trajectories, totalling a distance of 1,292,951km with a total duration of 50,176 hours. The dataset has a high resolution as 91.5% of the trajectories have been logged densely, i.e. every 1~5 sec. Furthermore, 62 of the users provided information on their mode of transportation for some of the trajectories. This means that we have labelled data at our disposal, which can be used to train various machine learning models. Consequently, these can be used for transportation mode prediction. There have been various approaches to this already, but there is still scope to explore more.

Inspired by the work accomplished by a group of researchers at Microsoft Research Asia, we began by identifying so-called stay points. A stay point is the average point of all points in a sequence where 1) the distance from the first point to each of the subsequent ones is less than a threshold distance, D, and 2) where the timespan between the first and last point in the sequence is above a certain threshold, T. In popular places there will be an agglomeration of stay points, pertaining to different users. Using unsupervised learning (clustering, in this case) it is possible to identify Points of Interest (PoI). These are the locations where many users tend to spend a certain amount of time, and hence are locations that can be perceived as interesting for one reason or another.

Currently, I am investigating ways in which we can gain insight into what user patterns can be found at the Points of Interest. Using this information I am studying what amenities, utilities, facilities etc. are present in the neighbourhood and what their relative distance to a PoI is. The method I am using is to look at how many different users have been to a PoI, determine the frequency of visits, the distribution of arrival and leaving times, the time spent there etc. This data can be used to provide us with information on why a place appears interesting and allows us to provide users with more targeted suggestions at a given PoI. For example, places of work and sleep can be inferred and matched with either companies or hotels and residential areas.

As is apparent by now, the project becomes considerably open-ended as one can work from various angles and gather a broad range of insights. The reader may notice that, so far, I have been using data that is neither particularly new, nor very local, to my immediate surroundings. What I have described here is merely a first step in a much larger effort. We are essentially using the GeoLife Trajectory dataset to create a query and analysis prototype. The framework that will emerge from this can then be used with other, newer data, including data that we will be collecting consensually here at Fetch.AI, through the Network Participation App which is connected to our ledger.

There are huge benefits to be gained from such an approach, but crucially, we also believe privacy and data security are extremely important. That’s why users of the Fetch.AI network will be able to select the data they choose to share with others.