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The Challenge

Privacy & Security

  • Personal data is collected unconsciously or without user consent
  • Users have no idea about who can access to the data and what they will do with it

Compensation

  • Data is the most precious asset in the digital society and its value continues growing
  • Users who generate data get no reward from the value their data creates

Regulation

  • Forthcoming privacy regulations e.g. GDPR could be a huge challenge for companies
  • There is no standard solution yet to deal with the challenge

Human-AI Solution

Human AI

  • Integrate human intelligence with machine intelligence to make AI smarter
  • Give users full access to and control of personal data
  • Users can set trade-offs between data privacy and service personalization

Open Algorithm

  • We collaborate with MIT Open Algorithms (OPAL) and extend it towards mobile devices
  • Raw data stays on users’ devices with publicly verifiable code running on top
  • Only aggregated knowledge or algorithm outputs will be shared with data consumers

Blockchain

  • Control access to personal data and user account in the ecosystem
  • Guarantee data integrity as well as data availability
  • Ensure secure token storage and automated payments

Human-AI Ecosystem

The ecosystem is separated into a user space and a service space by blockchain. The user space controls all interactions with end users and deals with data collection, aggregation, and encryption. Blockchain and insight storage take care of data transfer and token transaction. The service space, on the other hand, conveys aggregated knowledge and/or user feedbacks to data consumers and business applications.

sys_ovv

Prof. Alex Sandy Pentland, MIT

Co-founder
The way in which you can make groups of people smarter, the way you can make human AI, will work only if you can get feedback to them that's truthful.

The Human Strategy. A Conversation with Alex "Sandy" Pentland on Edge.org [10.30.17].

Personal Data and Use Cases

Mobile Personal Data

  • Behavioral data collected on smartphones
  • Such as a list of installed apps, app usage, GPS, sensor & activity logs
  • Can be used to develop AI models

Mobile App Data

  • Behavioral data within each mobile app
  • Hard to be shared and monetized due to forthcoming privacy regulations
  • Can be unlocked by users through our SDK

User Feedback Data

  • Feedbacks or answers to a questionnaire
  • Existing approaches are costly and hardly context-aware
  • Combine human with machine intelligence
Use Cases

An AR game publisher wants to do mobile marketing among users who have already played AR games. We can help it identify these users directly through checking app logs or indirectly through predicting personal interests based on other mobile data. Furthermore, how a user spends his/her time on various apps can be derived, which leads to knowledge about the user’s attention on smartphones and how it evolves over time.

The cryptocurrency market is growing rapidly and many investors use mobile apps like Coin Market Cap to track performance of investment. In-app personal data, such as each user’s portfolio, can be shared on social media but not yet monetized. With our SDK, anyone can publish his/her portfolio and users who want to follow top-performing investors will have access to the data after paying a fee to the data publisher.

A healthcare company hypothesizes that a user’s activity on smartphone well reflects the user’s stress. Human-AI framework can be utilized to gather data on how people use smartphones, but scientific rigor requires ground-truth to assess their stress levels. Our SDK facilitates this by sending users questionnaires to sample real-time stress levels. The company can develop models to predict stress and apply treatments to improve users’ mental health.

The Products

unlock-mobile-80

1. Unlocking Mobile Personal Data

Our first product aims at unlocking and monetizing mobile personal data on mobile devices. Each user’s mobile attention, which means how s/he spends time on different mobile apps, will be our focus in the beginning of development. Other types of mobile personal data such as location and movement, as well as user feedback data will also be unlocked in the product.

mobile-sdk -80

2. Mobile SDK to Scale up

The second product is a mobile SDK, with which businesses can integrate existing apps into the Human-AI ecosystem. It also helps them stay in compliance with new data regulations such as GDPR. The SDK provides the same features as the first product, but with extended data type, i.e., in-app data. Consequently, users will be able to share and monetize personal data in any mobile app.

unlock-vr-80

3. Moving towards AR / MR

We believe that AR/MR will become the next generation consumer electronics. In addition to smartphone data, AR provides opportunities for better user interaction and personalization. For example, it empowers eye tracking on the one hand and real-time image recognition on the other hand. This helps to understand more precisely about each user’s actual attention and interest.

Core Team

Advisors

The HAI Token

Characteristics

  • Token Symbol: HAI (ERC20 Token)
  • Hard Cap: $20 Million
  • Total HAI Created: 1,000,000,000
  • Max. Units of HAI Sold: 400,000,000
  • Pre-Sale Launch Time: 2018 Q2
  • Public-Sale Launch Time: 2018 Q3

40% Sold during TGE

25% User Adoption & Incentives

20% HumanAI (vesting over 2 years)

10% Partnerships & Community

5% TGE Costs & Bounty Program

Milestones & Roadmap

1. CTI Grant Fund for Research in Mobile Personal Data

Started research activities in mobile personal data and relevant business cases at ETH Zurich and University of St. Gallen. Grant fund from the National Commission for Technology and Innovation in Switzerland (CTI).

2014 Q3

1. CTI Grant Fund for Research in Mobile Personal Data

Started research activities in mobile personal data and relevant business cases at ETH Zurich and University of St. Gallen. Grant fund from the National Commission for Technology and Innovation in Switzerland (CTI).

2014 Q3

First Prototypes for Collecting Mobile Personal Data

Started to build first prototypes for collecting mobile personal data and released mobile apps in Google Play Store.

2015 Q1-Q4

First Prototypes for Collecting Mobile Personal Data

Started to build first prototypes for collecting mobile personal data and released mobile apps in Google Play Store.

2015 Q1-Q4

AI User Profiling Models on Mobile Personal Data

Development of AI user profiling models on mobile personal data.

2016 Q2-Q4

AI User Profiling Models on Mobile Personal Data

Development of AI user profiling models on mobile personal data.

2016 Q2-Q4

2. CTI Grant Fund for Research in Mobile Personal Data

Second grant fund from the National Commission of Technology and Innovation in Switzerland (CTI).

2016 Q3

2. CTI Grant Fund for Research in Mobile Personal Data

Second grant fund from the National Commission of Technology and Innovation in Switzerland (CTI).

2016 Q3

Large-Scale Field Studies for Business Case Validation

Validate business cases with large-scale field studies (75,000 users).

2016 Q4

Large-Scale Field Studies for Business Case Validation

Validate business cases with large-scale field studies (75,000 users).

2016 Q4

OPAL platform

Conception and Development of the OPAL system (paradigm of open algorithms) by the Human Dynamics group of Professor Alex “Sandy” Pentland at MIT Media Lab.

2017 Q1-Q4

OPAL platform

Conception and Development of the OPAL system (paradigm of open algorithms) by the Human Dynamics group of Professor Alex “Sandy” Pentland at MIT Media Lab.

2017 Q1-Q4

HumanAI White Paper

Completion of HumanAI White Paper

2017 Q4

HumanAI White Paper

Completion of HumanAI White Paper

2017 Q4

HumanAI AG and ICO Announcement

Founding of the company and announcement of the HAI token crowd sale.

2018 Q1

HumanAI AG and ICO Announcement

Founding of the company and announcement of the HAI token crowd sale.

2018 Q1

Unlocking Personal Smartphone Data for AI

Development and release of the first product.

2018 Q2 - 2019 Q3

Unlocking Personal Smartphone Data for AI

Development and release of the first product.

2018 Q2 - 2019 Q3

Cooperation Center for Digital Health Interventions

Start of cooperation with the Center for Digital Health Interventions at ETH Zurich and University of St. Gallen.

2019 Q1

Cooperation Center for Digital Health Interventions

Start of cooperation with the Center for Digital Health Interventions at ETH Zurich and University of St. Gallen.

2019 Q1

Mobile SDK to Scale Up

Development and release of the second product.

2019 Q3 - 2020 Q4

Mobile SDK to Scale Up

Development and release of the second product.

2019 Q3 - 2020 Q4

Moving towards AR/MR

Development of the third product.

2021 Q1-Q4

Moving towards AR/MR

Development of the third product.

2021 Q1-Q4

Scientific Publications

We have been researching topics of user behavior, data science, prediction models, AI, blockchain and privacy for several years. A central question of our research is how disruptive technology on mobile devices can change behavior of humans. The findings of our research will be incorporated into Human-AI.

Blockchain & Privacy

Frey, R.M., Bühler, P., Gerdes, A., Hardjono, T., Fuchs, K., Ilic, A., Proceedings of the 16th IEEE International Symposium on Network Computing and Applications (NCA), Cambridge, MA, USA. 2017

Privacy Preserving Data Certification in the Internet of Things: Leveraging Blockchain Technology to Secure Sensor Data

Chanson, M., Bogner, A., Bilgeri, D., Wortmann, F.,  Proceedings of the 38th International Conference on Information Systems (ICIS), Seoul, South Korea. 2017

Chanson, M., Bogner, A., Wortmann, F., Fleisch, E., Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, Maui, Hawaii. 2017

Frey, R.M., Wörner, D., Ilic, A., Proceedings of the 22nd Americas Conference on Information Systems (AMCIS), San Diego, CA, USA, 2016

Frey, R.M., Vuckovac, D., Ilic, A., Proceedings of the 10th ACM Conference on Recommender Systems (RecSys), Boston, MA, USA, 2016

Predictive Models from Mobile Devices

Frey, R.M., Xu, R., Ammendola, C., Moling, O., Giglio, G., Ilic, A., Information Systems 71 (2017) 152–163

Frey, R.M., Xu, R., Ilic, A., Pervasive and Mobile Computing 40 (2017) 512–527

Xu, R., Frey, R.M., Fleisch, E., Ilic, A., Computers in Human Behavior 62 (2016) 244–256

Xu, R., Frey, R.M., Ilic, A., Proceedings of IEEE BigDataService, Oxford, UK. 2016

Xu, R., Frey, R.M., Vuckovac, D., Ilic, A., Proceedings of the 23rd European Conference on Information Systems (ECIS), Münster, Germany. 2015

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Our Contacts

HumanAI AG
Alpenblick 10
6330 Cham, Switzerland

contact@HumanAI.co

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