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Humanity with Privacy and Integrity is Taiwan AI Mindset

The 2018 Smart City Summit & Expo (SCSE) along with three sub-expos have taken place at Taipei Nangang Exhibition Center on March 27th with 210 exhibitors from around the world this year, exhibiting a diversity of innovative applications and solutions in building a smart city. Taiwan is known for the friendly and healthy business environment, ranked as 11th by World Bank. With 40+ years in ICT manufacturing and top level embedded systems, companies form a vigorous ecosystem in Taiwan. With an openness toward innovation, 17 out of 22 Taiwan cities have made it to the top in Intelligent Community Forum (ICF).

Ethan Tu, Taiwan AILabs Founder, gave a talk of “AI in Smart Society for City Governance” and laid out AI position in Taiwan that smart cities is for “humanity with privacy and integrity” besides “safety and convenience”. He said “AI in Taiwan is for humanity. Privacy and integrity will also be protected.”. The maturity of crowd participation, transparency and open data mindset are the key assets to drive Taiwan on smart cities to deliver humanity with privacy and integrity. Taiwan AILabs took social participating and AI collaborated editing open-source news site of http://news.ptt.cc as an example. The city governments are now consuming the news to detect the social events happening in Taiwan in real-time for the AI news’ robustness and reliability in scale. AILabs collaborated with Tainan city on AI drone project to simulate “Beyond Beauty” director Chi Po-lin who dies in helicopter crash. AILabs also established “Taipei Traffic Density Network (TTDN)” supporting real-time traffic detection and prediction with citizen’s privacy secured, no people nor car can be identified without necessity for Taipei city.

The Global Solutions (GS) Taipei Workshop 2018 with “Shaping the Future of an Inclusive Digital Society” took place at the Ambassador Hotel on March 28, 2018 in Taipei. It is co-organized by Chung-Hua Institute for Economic Research (CIER) and the Kiel Institute for the World Economy. The “Using Big Data to Support Economic and Societal Development” panel section was hosted by Dennis Görlich Head, Global Challenges Center, Kiel Institute for the World Economy. Chien-Chih Liu, Founder of the Asia IoT Alliance (AIOTA), Thomas Losse-Müller, Senior Fellow at the Hertie School of Governance, Reuben Ng, Assistant Professor, and Lee Kuan Yew School of Public Policy, National University of Singapore all participated in the discussion. Big data has been identified as oil for AI and economic growth. He shared the vision in his panel, “We don’t have to sacrifice for safety or convenience. On the other hand, Facebook movement is a good example that the tech giants who overlook privacy and integrity will be dumped.”

Ethan explained 3 key principles from Taiwan societies on big data collection. The following principles exist and are contributed by the mature open internet societies and movements in Taiwan. AILabs will promote them as fundamental guidances for data collection on medical records, government records, open communities and so on.

1. Data produced by users belongs to users. The policy makers shall ensure no solo authority such as social media platform is too dominant to user and force users on giving up data ownership.

2. Data collected by public agent belongs to public. The policy makers shall ensure the data collected by public agency shall provide the roadmap on opening data for general public on researches. g0v.tw for example is a NPO for the open data movement.

3. “Net Neutrality” is not only ISP but also for social media and content hosting service. Ptt.cc for example, persists in equality of voice without Ads. Over the time the equality of voice has overcome the fake news by standing-out evidences.

“Humanity is the direction for AILabs. Privacy and Integrity are what we insist.” said Ethan.Smart City workshop with Amsterdam Innovation Exchange Lab from Netherlands

SITEC from Malaysia visiting AILabs.tw

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Meet JARVIS – The Engine Behind AILabs

In Taiwan AI Labs, we are constantly teaching computers to see the world, hear the world, and feel the world so that computers can make sense of them and interact with people in exciting new ways. The process requires moving a large amount of data through various training and evaluation stages, wherein each stage consumes a substantial amount of resources to compute. In other words, the computations we perform are both CPU/GPU bound and I/O bound.

This impose a tremendous challenge in engineering such a computing environment, as conventional systems are either CPU bound or I/O bound, but rarely both.

We recognized this need and crafted our own computing environment from day one. We call it Jarvis internally, named after the system that runs everything for Iron Man. It primarily comprises a frontdoor endpoint that accepts media and control streams from the outside world, a cluster master that manages bare metal resources within the cluster, a set of streaming and routing endpoints that are capable of muxing and demuxing media streams for each computing stage, and a storage system to store and feed data to cluster members.

The core system is written in C++ with a Python adapter layer to integrate with various machine learning libraries.



The design of Jarvis emphasizes realtime processing capability. The core of Jarvis enables data streams flow between computing processors to have minimal latency, and each processing stage is engineered to achieve a required throughput per second. For a long complex procedure, we break it down into smaller sub-tasks and use Jarvis to form a computing pipeline to achieve the target throughput. We also utilize muxing and demuxing techniques to process portions of the data stream in parallel to further increase throughput without incurring too much latency. Once the computational tasks are defined, the blue-print is then handed over to cluster master to allocate underlying hardware resources and dispatch tasks to run on them. The allocation algorithm has to take special care about GPUs, as they are scarce resources that cannot be virtualized at the moment.

Altogether, Jarvis becomes a powerful yet agile platform to perform machine learning tasks. It handles huge amount of work with minimum overhead. Moreover, Jarvis can be scaled up horizontally with little effort by just adding new machines to the cluster. It suits our needs pretty well. We have re-engineered Jarvis several times in the past few months, and will continue to evolve it. Jarvis is our engine to move fast in this fast-changing AI field.


Featured image by Nathan Rupert / CC BY

AI carries the torch of Malaria diagnosis in Taiwan

Meeting the shortage of medical technologist, our AI is learning from the experience of medical experts in Taiwan CDC to bring expert-level precision and speed to the diagnostic process of Malaria in Taiwan.

Taiwan has been on the list of Malaria-eradicated regions since 1965. Since then, there have been around 10 – 30 malaria cases each year, all of which are imported cases, as reported by Taiwan CDC (Centers for Disease Control). Due to the declining number of Malaria cases, there have been fewer medical laboratory technologists specialized in Malaria diagnosis, while the training of new experts is becoming increasingly difficult.

It is said that the most experienced medical laboratory technologist in Taiwan CDC, who has been in charge of Taiwan’s Malaria diagnosis for years, is retiring soon. There was never a single misdiagnosed case in her hand. She is concerned that her experience and knowledge might not be able to pass on to the future generations.

Thanks to the recent advancement of artificial intelligence, computers now have the potential to learn from her experience of medical expertise and lead a pivotal role in the Malaria diagnostic process. We are now getting the ball rolling by collaborating with Taiwan CDC on the Malaria Diagnostics Project to leverage AI to improve the diagnosis process of Malaria.


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How AI is Transforming Personalized Treatment: scientists are training AI to classify the effects of genetic mutations

Recently, utilizing personal information to tailor personalized treatment has gained a lot of attention. In the case of cancer, the disease begins when one or more genes in a cell are mutated. This makes each tumor distinct even if it comes from the same cell, therefore the result of a treatment may vary on different patients. With identifying genetic mutations on a patient, doctors can find out what the cause of a tumor is and give accurate treatment.

Identifying genetic mutations is becoming easier, but interpreting it remains difficult. For breast cancers, there are about 180 oncogenic mutations that contributes to the growth of tumor. To distinquish them from normal variants requires examining literature carefully. While there are thousands of publications studying the effect of genetic mutations, these information cannot be used efficiently due to lack of well-curated databases. Building such database is a costly process that requires experts to review clinical literature manually. According to Memorial Sloan Kettering Cancer Center(MSKCC), they organized an annotation committee to review data from different sources, and spent 2 months to annotate 150 genetic mutations, while there are 79 million mutations have been identified by the 1000 Genomes project. Furthermore, the number of publications is growing exponentially, so a automated classification process is in demand. To speed up the curation of mutation databases, we utilized machine’s ability to read and comprehend, which can efficiently review publications and classify the effect of genetic mutations.

From reading to comprehending

Our goal is to train a machine that can classify the mutations like human experts. Instead of training a general comprehension model, we want it to imitate the decision making procedure of experts since the amount of annotated data is insufficient.

Extract key paragraphs

Reading a 10-page paper from the beginning to the very end is time-consuming. People tend to go through the general description roughly and read the key paragraphs carefully. These paragraphs serve an important role since a sentence with mutation names in it may directly conclude the effect of the mutation. To imitate this behavior, we find keywords in the text and extract its context as key paragraphs for further investigation.

Vector representation

It is very common that words or ducuments are encoded into vector space embeddings before being processed by machine learning models. Recently, models did a great job finding representation for words. We use Word2Vec model to extract vector representation of gene names and mutation names, which are expected to be informative about its effect.

In the primary experiments, the model showed positive result as it can succesfully distinquish oncogenic mutations from normal ones. We headed to Kaggle competition [Personalized Medicine: Redefining Cancer Treatment] for a more controlled environment.

Kaggle Competition

In June 2017, MSKCC launched a Kaggle competition named “Personalized Medicine: Redefining Cancer Treatment“. The participants were asked to predict the effect of a genetic mutation given relevant documents. We collaborated with domain experts to participate this competition.

To get a basic idea of the documents, we use Doc2Vec model implemented in gensim to obtain vector space embedding of each document. The embeddings are then projected into two-dimensional space using PCA transformation. The resulting plot shows that documents from different classes can be roughly separated by its content.


Doc2Vec embeddings with PCA transformation into 2D space


Solution to small-data problem

The dataset is relatively small with about 3000 entries; therefore we focus on feature engineering part and keep our model simple to overcome the overfitting problem. We extract classical features, such as tf-idf values, along with engineered features based on observation and domain knowledge. Keywords suggested by experts are used to extract key paragraphs, where human experts pay more attention while determining the effect of mutations.


An example of key paragraph(marked yellow)



A powerful model XGBoost is used as classifier here. XGBoost has won pratically every competition in the structured data category over the last two years. In addition to strong modeling ability, its regularization technique is also well-suited for the dataset.


We obtained 75% accuracy among 9 classes on the test set. The competition host also held another stage with a very different test set. In this stage, the training/testing set mismatch is too significant, makes all participants’ classifier nearly unusable.


The problem has not been well-defined. Biases in the annotating process and the ambiguity between classes has not been resolved. But the result shows that with carefully defined target, data-driven methods can be utilized to clasify the effect of genetic mutations.


Featured image by Dave Fayram / CC BY