Global demand for tech hardware has surged (partly) due to COVID-19 and the contactless economy. Looking to the future, Taiwan wants to expand it's resume by cultivating a world-class AI ecosystem. That's where the AI NEXT program comes in -- a series of online events designed to help all kinds of businesses, people in tech, and investors enter the global AI ecosystem and connect with Taiwanese AI companies. This first online forum hosted by the AI NEXT program, 2020 AI NOW Online Tech Forum EP1: AI in the Contactless Economy features talks from 6 directors and CEOs about their AI innovations in manufacturing, retail, and mobility. What do these 6 AI innovators all have in common?
The impacts of the recent COVID-19 pandemic on global trade and our daily lives have led to an acceleration of technological innovations in different sectors. At the same time, our society is transitioning from the era of Big Data towards Hyper Digitization. To help industries prepare for these trends and navigate the post-pandemic world, this year's Taiwan Innotech Expo (TIE 2020) was highlighting the latest smart living technologies that can spark new imaginations. Since its transformation into a global trade show, the TIE continues to draw international attention to Taiwan's strength in R&D and innovation. This year's event showcases how Taiwan stays resilient in face of a global crisis.
Echoing the voice of all tech innovators and government agencies, global leaders at Taiwan have pitched AI ML solutions for COVID-19 and the contactless economy. Today, our lives are controlled by promising innovations to drive the contactless economy. The global economy has slowed down and shrunk significantly in the last six months due to lockdown protocols. The COVID-19 pandemic has been the single biggest catastrophe to have derailed the global scenario. While we were aware of mostly contactless payments and digital messaging solutions making it big in the Pre-COVID-19 days, today's scenario is totally different.
When Chih-Han Yu's work on multi-agent artificial intelligence (AI) was nominated as the best doctoral thesis of the year in 2010, the rising star in AI was not content with his stellar achievements, which included an early prototype self-driving car that laid the foundation for Google's self-driving car project. "People knew we were publishing high-quality research, but back in my dorm, my room-mate and I were thinking that we've worked on all this coding, but we have never seen any algorithm that has really impacted the world and transformed how people live and how business is done," said Yu, referring to his time at Harvard University. The duo decided they should do something and started a company specialising in AI-powered game engines that mimic the actions of human gamers, based on Yu's doctoral thesis. But that proved to be a mistake, said Yu, because there was no demand for the technology at the time. Two years later, they pivoted the business that would later become Appier, a supplier of AI-based marketing technology that helps businesses improve customer engagement and drive sales at a time when interest in big data was growing.
TAIPEI, Taiwan – AAEON Technology in Taipei, Taiwan, and Aotu.ai in Santa Clara, Calif., are introducing the BrainFrame Edge AI Developers Kit (DevKit) for an Intel artificial intelligence (AI) computer to enable system integrators rapidly to create and deploy smart machine vision applications. The BrainFrame Edge AI DevKit helps create solutions such as machine vision-based access control, uniform compliance, manufacturing automation, and video analytics. BrainFrame scales and configures easily and enables a connected camera to become a continuously monitoring Smart Vision system. BrainFrame's automatic algorithm fusion and optimization engine has VisionCapsules, Aotu.ai's open source algorithm packaging format. These self-contained capsules have a negligible memory footprint and include all necessary code, files, and metadata to describe and implement a machine learning algorithm.
By Sumit Pandey Taoyuan City (Taiwan), Sep 20 (UNI) Even as the world awaits a COVID vaccine, Artificial intelligence (AI) can be used for detecting pneumonia caused by the pandemic which has claimed nearly a million lives globally. The dataset commonly used for this work is open source chest X-ray images from Kaggle or other open-source websites. Some of these models have reported an accuracy even greater than 98 percent, experts have said. The experts while calling for integrating the AI systems into the medical practice, said it would build a mutually-beneficial relationship between AI and Medicine. In future AI would offer greater efficiency or cost-effectiveness and Doctors (or Medical Staff) would offer AI the essential medical exposure of complex cases.
When the whole world seems to come to a standstill because of the COVID-19 pandemic, the two sectors that have become extremely critical are agriculture and healthcare. Agriculture is the backbone of any economy, and it has become all the more important during this crisis to keep the global food supply chain running smoothly to ensure that there is no food crisis. On the other hand, the healthcare sector is the most stressed out sector right now that is working hard to ensure the health and safety of the population. While technology has played a critical role in ensuring business continuity across various sectors, let us take a look at how the latest technologies like AI and ML are set to transform these two critical industries. AI in Agriculture The food crisis has plagued the world for long. Add on top of that the havoc being unleashed on agriculture due to climate change, and we have a ripe case for AI and ML application to solve the food crisis. This is being touted as the next major agricultural revolution. By leveraging such next-generation technologies and by working smarter, a lot of ground can be covered. It is estimated that the AI in the agriculture market will grow to USD 4.0 billion by 2026. So, what are the various use cases of AI in agriculture? Let’s take a look Yield Optimization: Yield optimization is beyond just historical data analysis. There are components such as data about weather predictions, soil data, and even the economic conditions of the regions which come into play. Using AL/ ML models to analyze these parameters, farmers can not only predict the yield but also know how to optimize the same. Livestock Management: Cattle and livestock are important assets for farmers. They not only help in the farming process but also provide other sources of income in the form of meat as well as dairy. Their health is of utmost importance, and the whole herd can be affected adversely by diseases of foot and mouth. Farmers now deploy infrared sensors and other smart monitors to detect anomalies in the herd movement or temperature reading to identify and cure the particular animal and prevent the disease from becoming a catastrophe. Weed Detection: The same principle can be extended to crop disease and weed detection. Farmers can use smart devices, robotics, and machine learning to constantly monitor and identify the changes in the vitals of the crop and see if unwanted weeds are growing on the soil. AI bots can help farmers cull out the weed at the nascent stages and harvest a higher volume crop at a faster pace. Soil and Water Management: AI-powered smart metering can help farmers in the economical usage of water resources and help them in cost-saving. Similarly, these technologies can help them with the optimal utilization of soil. Over usage of the soil can lead to depletion of its natural resources and optimal usage can ensure that the soil retains its vitality. By adopting AI and ML, farmers can know when the soil might need replenishment and how the intervals need to be spaced out considering the weather, yield, and soil time. This can help them make more accurate decisions regarding sowing, seed selection, and fertilizer usage to get a higher yield per hectare. AI in Healthcare AI has come a long way in healthcare and has already delivered a high impact on this industry. It has played a pivotal role in giving rise to a patient-centric model. Here are some of the many ways AI and ML are transforming the healthcare industry – Drug Discovery: On average, $2.7 billion are spent by the pharmaceutical companies for every drug. Pharma companies have started leveraging AI in drug design to better predict molecular dynamics. It is helping them improve development efficiency and reduce drug development costs. Improve Operational Efficiencies: Hospitals need to optimally manage various parameters such as temperature, humidity, and air regulation to run the facilities smoothly. AI is allowing hospitals to smoothly carry out facility management while ensuring the physical safety of the patients and staff members. These technologies are also enabling predictive maintenance of hospital assets and the tracking of healthcare devices to ensure proper allocation and deliver better care outcomes. Pandemic Management: Countries like Taiwan, Japan, and Singapore leveraged the power of AI and ML to curtail the spread of coronavirus. Borrowing from the principles of how communicable diseases spread, AI/ ML helped in predicting the spread of the coronavirus and allowed the government agencies to put in place the required logistics, ensure border controls, and protect their most vulnerable staff members. Remote Health Monitoring: Remote health monitoring and telemedicine have shifted care from a hospital to a more personal environment, such as the patient’s home. It is helping patients in saving their care costs and also helping in reducing the workload on hospitals. With wearable devices monitoring the basic vitals of a patient and periodically streaming those to the caregivers, the quality of care has improved. Precision Surgeries: AI robots are not new anymore. Those are being increasingly adopted in hospitals for carrying out precision surgeries. These medical robots are also being used in rehabilitation facilities. Today, a large volume of data is being generated from various sources. The future will be driven by technologies like AI and ML and their ability to crunch this data to deliver actionable insights. These insights will have a significant impact on the lives of people!
During my internship at Taipei Medical University, I was working on developing predictive models for Chronic Kidney Disease. The project was an educational experience with challenges that arose from the complexity and large size of the dataset. In this article, I will share the key lessons I learnt that helped me boost my productivity as a beginning researcher. The ideas are quite general and applicable to most machine learning workflows. Some of them are obvious ones that we choose to ignore.
The company said the chips use a new manufacturing technique and other tweaks that will make them more powerful at tasks such as using artificial intelligence to reduce background noise during video calls. The company said it worked with laptop makers including Dell Technologies and Samsung Electronics Co Ltd and that there will be 50 different machines from different makers available for the holiday shopping season. The Tiger Lake processors come as Intel, one of the few chip companies that both designs and makes its own chips, has struggled with manufacturing delays. The company has started to lose market share to rivals such as Advanced Micro Devices Inc that use outside manufacturers such as Taiwan Semiconductor Manufacturing Co .