Fetch is building AI protocol which enables economic activities to happen automatically. Fetch’s technology has a structure of three layers. The highest layer includes digital agents, AEAs (Autonomous Economic Agents) which extract data from hardware system and data resources and then provide it for those who are in need. The agents exist in the decentralized virtual world created by OEF (Open Economic Framework). This OEF is supported by the Fetch smart ledger which can scale up to tens of thousands of TPS.

Technological ideas

Fetch is building AI protocol which enables economic activities to happen automatically. Fetch’s technology has a structure of three layers. The highest layer includes digital agents, AEAs (Autonomous Economic Agents) which extract data from hardware system and data resources and then provide it for those who are in need. The agents exist in the decentralized virtual world created by OEF (Open Economic Framework). This OEF is supported by the Fetch smart ledger which can scale up to tens of thousands of TPS.

Tens of thousands of TPS is reachable due to a kind of innovative sharding. Instead of sharding schemes, Fetch smart ledger has transaction lanes and a transaction may be assigned to different transaction lanes so that new lanes created can refer back to the old lane’s block. Apart from that feature, Fetch Smart Ledger uses useful Proof of Work, a unique consensus system to perform the computing itself. While conventional PoW requires a huge amount of resources to get the right to create a block as in the Bitcoin network, computational resources will be used to train machine learning algorithms and deliver a decentralized supercomputer which will help to allocate computational work based on the capacity of the network’s nodes.

AI application is extremely hot in the cryptocurrency market now such as AI for real estate, AI for personal data protection or AI for music industry. However, some projects which combine AI and blockchain technology to data exchange have not received expected results. Therefore, if Fetch.AI can accomplish its technological development, it will be really potential regarding the demand for using and exchanging data and digital assets currently.

3. Team

- Humayun Sheikh (Co-founder – CEO): founding investor in Deep Mind Deep Mind is the world leader in artificial intelligence research and its application. Deep Mind created a neural network that learns how to play games like humans do. The company was founded in 2010 and Google bought it in 2014. Deep Mind now is a part of the Alphabet group. He is also Founder and CEO of uVue , a company building a distributed automated transportation network and itzMe , a company applying Machine Learning&Artificial Intelligence to deepen to interaction between people and digital services. However, the information about those two companies is limited.

- Toby Simpson (Co-founder – CTO): He used to work as CTO or CEO at Nice Tech Limited but there is no information about this company. Additionally, he was CTO of Ososim Limited, a global learning and technology company that applies AI to business. Ososim Limited is not a famous company. And he spent 2 years working as Head of Software Design at Deep Mind.

- Thomas Hain (Co-founder – CSO). He has Ph.D. degree from Cambridge. During 8 years at University of Shieffield , top five universities in UK , the first was Professor of Speech and Audio Technology, then was Head of Speech and Hearing Research.

- Troels Frimodt Rønnow (Senior Software Engineer): He has experience of more than 3 years working at Nokia as Senior Researcher, Principal Researcher and Research Leader. It is mentioned in LinkedIn that he was the owner of Wonop. Ltd but there is no information referring to this company.

- Jonathan Ward (Senior Machine Learning Scientist): He gets PhD in machine learning from University College London. He is a researcher in AI. From 2005 – 2015, he worked at EMBL (European Molecular Biology Laboratory) as research scientist. EMBL is a molecular biology research institution supported by 22 member states, 4 prospect and 2 associate member states. It is one of the world’s leading research institutions, and Europe’s flagship laboratory for the life sciences. This institution is really prestigious. He is also the senior algorithms engineer at DNA Electronics, a pioneering molecular diagnostics company developing solutions which enable rapid near-patient, real-time diagnostic. This company is famous in UK.

- Jerome Maloberti (Machine Learning Developer): He was software engineer at Become Japan, an award-winning provider of cloud-based performance marketing and SaaS solutions. Apart from that, he was a consultant at Fusion, one of the world's leading providers of IT solutions and business consultancy, a software engineer at Independent and research engineer at Université Paris Diderot. From 2013 to 2016, Jerome Maloberti was Senior software engineer at Linguamatics, the company deploying innovative natural language processing (NLP)-based text mining for high-value knowledge discovery, information extraction and decision support and Broadcom, a designer, developer and global supplier of products based on analog and digital semiconductor technologies; and principal software engineer at Cadence Design Systems, an American multinational electronic design automation (EDA) software and engineering services company. Before working for Fetch.AI, he was the core developer at Grapeshot a company using Advanced Keyword Technology to segment inventory and improve targeting, making advertising welcome.

- The team also includes Arthur Meadows (Commercialisation Director), Sebastian Nickle (Machine Learning Researcher), Shaishav T. (CTO and Simon Clifford (Communications Systems professional). The team has 15 members in total. However, the number of team members is increasing because Fetch. AI wants to recruit more to develop its technology.

CEO of Fetch.AI seems to be an investor who is really interested in AI and blockchain. Although the CTO’s experience is not directly involved in blockchain and AI, the CSO has an academic understanding about technology. More importantly, the founders can gather a team with great skills and experience in technology and especially AI and Machine Learning. There are many IT engineers and researchers in the core team. A lot of them used to work for high-profile companies or organizations such as Deep Mind, EMBL or Cadence Design Systems.

Community

- Telegram: 5,441 members

- Twitter: 1,132 followers

- YouTube: 131 subscribers

- LinkedIn: 179 followers

The number of members in Telegram group chat is increasing. Recently, the number of Fetch.AI in Telegram group has increased from 3,817 on June 2 to 5,441 on June 4 although the increase rate was quite low before. Also, it is not easy to find articles about Fetch.AI project from reliable sources except for Medium.

Summary

Strength

Fetch team is excellent considering their experience and skills in AI and Machine Learning It can be seen that the team invests seriously in technology because the major part of the team has expertise in technology. Therefore, their technological development is really promising.

uPoW created by the Fetch team will work effectively in order to prevent the waste of energy which was a problem of conventional PoW consensus protocol.

Also, according to an article review in Medium, Fetch.AI has already raised $15ml and aims to get about $30ml or $35ml. The review also mentioned that ‘Fetch.AI is strategically joining BMW, Ford, General Motors, Renault, Accenture and IBM to form the Mobility Open Blockchain Initiative (MOBI)’. The partnership with popular and prestigious enterprises will create a chance for Fetch.AI to expand its application, especially in the field of transportation. More importantly, with those big partners, the profile of Fetch.AI will become more reliable in the eyes of investors and the project can have a solid financial foundation to develop its project.

Article link: https://bit.ly/2JnnApj

Things to note

AI is becoming increasingly popular in ICO projects such as XAIN (AI for transportation service offerings and peer-to-peer sharing), DeepCloud AI (AI for running decentralized applications — IoT and Web 3.0 Dapps) and Surety.AI (AI and blockchain for insurance). More importantly, Google, Facebook and IBM greatly concern Big Data and AI. Therefore, Fetch.AI is facing great competition in the market.

Building community now is one of the most decisive factors for ICO projects to be successful after being launched. Even it needs more time for the project to release its testnet, Github or MVP, expanding community from early stages will give the project more support. However, considering the team of Fetch.AI, it seems that they do not pay much attention to marketing and community care now. So what if the community is attracted by one AI project which has effective marketing strategies and then become indifferent with other AI projects?

It is understandable that Fetch.AI wants to expand its team size to push the progress of the project. However, it should publish and update the profiles of its members properly in LinkedIn instead that it just lists the members’ names in its LinkedIn. It may cause investors and the community to question the solidarity and the commitment to the project of team members.

There is much more information needed to decide whether it is worth investing in Fetch.AI. In case Fetch.AI can release its MVP/ testnet before ICO and also publish its roadmap as soon as possible, Fetch.AI is a potential project for investors.

Rate from Icogens:

HYPE RISK ROI TERM ICOGENS’S SCORE MEDIUM LOW NOT RATE LONG MEDIUM

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Source: icogens.com