Software and data culture for AI system integration are required in Taiwan

In Taiwan, the industry doesn’t value much on software engineering and data science. More companies are good at chip manufacturing and hardware production. However, in the coming AI trend, is Taiwanese companies ready for catching up the trend? Whether the mindset of top managers can be changed and managers keep learning will decide whether Taiwan can catch up the world trend. Essentially, AI is about software, it analyzes data.

Fig 1. Machine Learning V.S. Manufacturing ,Source

Data Science (Machine Learning Research) stepped on ML Engineer and Data Engineer. It even not includes the most commonly seen Software Engineer. Data Engineer is the foundation, he takes care of collecting user behavior data from IoT devices, web platform, and App. The data is stored in Data Lake. Taiwan AI Academy recently published a survey. Data quality and hard-to-get data are the most seen difficulties. Jason stayed in Analytics Platform team while he was a software engineer at eBay. There were more than 200 people in that team, who are responsible for maintaining Data Platform. People were doing data collection, cleaning data, ETL jobs, A/B testing, ML model development,…etc.

Fig 2. Machine Learning Skills Pyramid,Source

Before introducing AI, why not thinking what “human intelligence” can do? Take the followings as examples, video surveillance, image object identification, audio recognition, and auto-driving. If we let human do those things, can we provide valuable service? If the value coming out from service can make quality improvement, we can start to automate the process through AI.

In Taiwan, there are a lot of well-trained Computer Science graduates every year. However, most of them join hardware industry. In 2018, international Tech companies, such as Google, Microsoft, Amazon, IBM, Line, and Oath Yahoo started hire AI engineers. Hopefully, this can bring some changes. In AI field, there are not many people with Software and Data experience. For new hire AI engineers, they might spend time to bump into several problems. Even if for managers with AI consciousness, hiring AI engineers/researchers, when they see slowing progress, bad effect, and bad results. A lot of times, they don’t know how to improve it. Therefore, the project was failed.

Some people might worry about that Taiwan is a small country. There is no data advantages. If you look at the growth speed of data in the world, a lot of data is collected recently. The number grows exponentially. Even if the data is collected before, it’s not clean enough for analysis. Let along saying no enough data.

What AI can bring in is helping enterprises increase value internally. Take the telecom industry as example, it leverages AI to reduce bad debts made by carrier billing from Google Play. Banks started to hire AI specialists for customer analysis by NLP preparing for Bank 4.0 transition. People engaging with banks happened more often on Web-bank, Mobile App than in physical banks. There is also interaction channel in Line. When there are multiple engaging channels with customers. The data is collected and merged into one analytics platform. Through customer behavior analysis, banks can recommend products and know more about what functions customers used. Through ML prediction and data analysis, it can bring in better service quality.

Traditional statistics model or open-source model can only bring the performance to a certain level. To make breakthrough, it must bring in ML mindset, making its own model. Most of the time, when AI model was developed in the beginning, the performance is not very good. It takes efforts to improve. Through feature extraction, model selection, and parameter tuning to improve performance. Take the global well-known ImageNet Challenge as example, the AI bypass human in terms of classification error in 2015. Since then, human won’t never be able to catch up. Take AlphaGo as an example, in the beginning, it loses to real human. Through the collaboration with ML researcher/ ML engineer/ Data Engineer, it finally beats human beings.

Fig 3. ImageNet Challenge Trend,Source

In Taiwan, there are some companies making sensor chips. If there is good system integration capabilities, by integrating software and hardware for data collection/ data pipeline, it’s very likely to sell products/services to the world. It can collect and store data in data centers or cloud platform. By leveraging ML algorithm and data platform, it can solve customers’ pain points in certain scenario. By leveraging the existing strength in hardware manufacturing with software/hardware integration, it can provide end-to-end total solution. There are two good examples in video surveillance industry. Umbo CV (B2B) sells product/service to other business. Deep Sentinel (B2C) sells product/service to end customers.

AI+healthcare can alleviate medical doctors’ burden in medical images. Senior medical doctors are usually asked to read the images. If the experience from senior doctors can be learned by AI, the healthcare quality can be increased with reducing man power. There was a report that Stanford researchers had good results in diagnosing Alzheimer’s disease by medical images. It can bring the diagnosis years before hand. In manufacturing factories, some IoT devices can be installed for industry 4.0. It’s possible to reduce man power in defect inspection via AI. It’s possible to reduce the risk of stopping machines for maintenance prediction via AI.

Software engineers in Taiwan are excellent. Otherwise, it wouldn’t attract some international technology companies recruiting employees in Taiwan. If we can integrate hardware and software with good system architecture design, we still can catch up the international AI wave. Take the following as a good example, in 2018 Google Play voting for most popular apps, there are small development teams with less than 10 people got elected. In Taiwan, the software development capability is strong. With it, it depends on whether higher managers can see through how much value software engineers can bring in. Software engineers should be valued.

In machine learning, there are training phase and prediction phase. There are many hardware embedded system companies in Taiwan. They are happy to see the opportunities of bringing AI via edge computing. However, the real value comes from increasing recognition/prediction accuracy. It needs big data platform for training. It takes time to tune parameters. During the learning/training phase, it still require software big data. After learning, it can compress and deploy the model to edges. Take voice recognition as an example, there are related applications, such as smart speaker and real-time translation. It requires a lot of vocabulary for training. There are vocabulary differences between Taiwan and China. There is locality issue in data. With time flies by, new vocabulary might come out, it requires software continuous learning for update, then deploy to edges.

Fig 4. Training and Prediction system,Source

Manufacturing industry requires integration for hardware components. Each component follows spec for doing its own thing. Software data service requires consideration for end-to-end whole system. Otherwise, it might get garbage in and garbage out. The value of software service is to find pain points and solve them. The future will be the trend for software service. Netflix, Spotify , iQiyi, and cloud service are charged by monthly usage. It’s very similar to familiar water/electricity bill and mobile phone internet bill.

It’s very likely to burn cash while introducing AI. It’s not easy to recruit AI engineers. It should be said that there are fewer people with AI experience. Companies spend money to send engineers and managers for AI training. After the AI training, it is still possible to get nothing while running projects. It doesn’t come out with what was expected. Many things need to be done correctly for success for AI projects. If enterprises are thinking about introducing AI into the flow, maybe it’s better to find professional AI architects with multiple year experience to help on planning to reduce risk.

Take self-driving car as an example. It’s a very expensive R&D. It needs to collect a lot of video data. Make some prediction for complicated scenes through ML. US technology companies already spent a lot of money, human power, and resources for R&D. Everything is possible. It depends on how much resources that higher-level managers are willing to throw in to solve what kind of problems. How long can the money bring back return. Investors should not just look at the quick money. It’s too short-sighted.

Reference

[1]https://www.facebook.com/ai4quant/posts/385788271986151


Originally published at ai4quantblog.com on January 6, 2019. Edited on January 21, 2019.