Tag Archives: Andrew Ng

Robot radiologists (artificially intelligent doctors)

Mutaz Musa, a physician at New York Presbyterian Hospital/Weill Cornell (Department of Emergency Medicine) and software developer in New York City, has penned an eyeopening opinion piece about artificial intelligence (or robots if you prefer) and the field of radiology. From a June 25, 2018 opinion piece for The Scientist (Note: Links have been removed),

Although artificial intelligence has raised fears of job loss for many, we doctors have thus far enjoyed a smug sense of security. There are signs, however, that the first wave of AI-driven redundancies among doctors is fast approaching. And radiologists seem to be first on the chopping block.

Andrew Ng, founder of online learning platform Coursera and former CTO of “China’s Google,” Baidu, recently announced the development of CheXNet, a convolutional neural net capable of recognizing pneumonia and other thoracic pathologies on chest X-rays better than human radiologists. Earlier this year, a Hungarian group developed a similar system for detecting and classifying features of breast cancer in mammograms. In 2017, Adelaide University researchers published details of a bot capable of matching human radiologist performance in detecting hip fractures. And, of course, Google achieved superhuman proficiency in detecting diabetic retinopathy in fundus photographs, a task outside the scope of most radiologists.

Beyond single, two-dimensional radiographs, a team at Oxford University developed a system for detecting spinal disease from MRI data with a performance equivalent to a human radiologist. Meanwhile, researchers at the University of California, Los Angeles, reported detecting pathology on head CT scans with an error rate more than 20 times lower than a human radiologist.

Although these particular projects are still in the research phase and far from perfect—for instance, often pitting their machines against a limited number of radiologists—the pace of progress alone is telling.

Others have already taken their algorithms out of the lab and into the marketplace. Enlitic, founded by Aussie serial entrepreneur and University of San Francisco researcher Jeremy Howard, is a Bay-Area startup that offers automated X-ray and chest CAT scan interpretation services. Enlitic’s systems putatively can judge the malignancy of nodules up to 50 percent more accurately than a panel of radiologists and identify fractures so small they’d typically be missed by the human eye. One of Enlitic’s largest investors, Capitol Health, owns a network of diagnostic imaging centers throughout Australia, anticipating the broad rollout of this technology. Another Bay-Area startup, Arterys, offers cloud-based medical imaging diagnostics. Arterys’s services extend beyond plain films to cardiac MRIs and CAT scans of the chest and abdomen. And there are many others.

Musa has offered a compelling argument with lots of links to supporting evidence.

[downloaded from https://www.the-scientist.com/news-opinion/opinion–rise-of-the-robot-radiologists-64356]

And evidence keeps mounting, I just stumbled across this June 30, 2018 news item on Xinhuanet.com,

An artificial intelligence (AI) system scored 2:0 against elite human physicians Saturday in two rounds of competitions in diagnosing brain tumors and predicting hematoma expansion in Beijing.

The BioMind AI system, developed by the Artificial Intelligence Research Centre for Neurological Disorders at the Beijing Tiantan Hospital and a research team from the Capital Medical University, made correct diagnoses in 87 percent of 225 cases in about 15 minutes, while a team of 15 senior doctors only achieved 66-percent accuracy.

The AI also gave correct predictions in 83 percent of brain hematoma expansion cases, outperforming the 63-percent accuracy among a group of physicians from renowned hospitals across the country.

The outcomes for human physicians were quite normal and even better than the average accuracy in ordinary hospitals, said Gao Peiyi, head of the radiology department at Tiantan Hospital, a leading institution on neurology and neurosurgery.

To train the AI, developers fed it tens of thousands of images of nervous system-related diseases that the Tiantan Hospital has archived over the past 10 years, making it capable of diagnosing common neurological diseases such as meningioma and glioma with an accuracy rate of over 90 percent, comparable to that of a senior doctor.

All the cases were real and contributed by the hospital, but never used as training material for the AI, according to the organizer.

Wang Yongjun, executive vice president of the Tiantan Hospital, said that he personally did not care very much about who won, because the contest was never intended to pit humans against technology but to help doctors learn and improve [emphasis mine] through interactions with technology.

“I hope through this competition, doctors can experience the power of artificial intelligence. This is especially so for some doctors who are skeptical about artificial intelligence. I hope they can further understand AI and eliminate their fears toward it,” said Wang.

Dr. Lin Yi who participated and lost in the second round, said that she welcomes AI, as it is not a threat but a “friend.” [emphasis mine]

AI will not only reduce the workload but also push doctors to keep learning and improve their skills, said Lin.

Bian Xiuwu, an academician with the Chinese Academy of Science and a member of the competition’s jury, said there has never been an absolute standard correct answer in diagnosing developing diseases, and the AI would only serve as an assistant to doctors in giving preliminary results. [emphasis mine]

Dr. Paul Parizel, former president of the European Society of Radiology and another member of the jury, also agreed that AI will not replace doctors, but will instead function similar to how GPS does for drivers. [emphasis mine]

Dr. Gauden Galea, representative of the World Health Organization in China, said AI is an exciting tool for healthcare but still in the primitive stages.

Based on the size of its population and the huge volume of accessible digital medical data, China has a unique advantage in developing medical AI, according to Galea.

China has introduced a series of plans in developing AI applications in recent years.

In 2017, the State Council issued a development plan on the new generation of Artificial Intelligence and the Ministry of Industry and Information Technology also issued the “Three-Year Action Plan for Promoting the Development of a New Generation of Artificial Intelligence (2018-2020).”

The Action Plan proposed developing medical image-assisted diagnostic systems to support medicine in various fields.

I note the reference to cars and global positioning systems (GPS) and their role as ‘helpers’;, it seems no one at the ‘AI and radiology’ competition has heard of driverless cars. Here’s Musa on those reassuring comments abut how the technology won’t replace experts but rather augment their skills,

To be sure, these services frame themselves as “support products” that “make doctors faster,” rather than replacements that make doctors redundant. This language may reflect a reserved view of the technology, though it likely also represents a marketing strategy keen to avoid threatening or antagonizing incumbents. After all, many of the customers themselves, for now, are radiologists.

Radiology isn’t the only area where experts might find themselves displaced.

Eye experts

It seems inroads have been made by artificial intelligence systems (AI) into the diagnosis of eye diseases. It got the ‘Fast Company’ treatment (exciting new tech, learn all about it) as can be seen further down in this posting. First, here’s a more restrained announcement, from an August 14, 2018 news item on phys.org (Note: A link has been removed),

An artificial intelligence (AI) system, which can recommend the correct referral decision for more than 50 eye diseases, as accurately as experts has been developed by Moorfields Eye Hospital NHS Foundation Trust, DeepMind Health and UCL [University College London].

The breakthrough research, published online by Nature Medicine, describes how machine-learning technology has been successfully trained on thousands of historic de-personalised eye scans to identify features of eye disease and recommend how patients should be referred for care.

Researchers hope the technology could one day transform the way professionals carry out eye tests, allowing them to spot conditions earlier and prioritise patients with the most serious eye diseases before irreversible damage sets in.

An August 13, 2018 UCL press release, which originated the news item, describes the research and the reasons behind it in more detail,

More than 285 million people worldwide live with some form of sight loss, including more than two million people in the UK. Eye diseases remain one of the biggest causes of sight loss, and many can be prevented with early detection and treatment.

Dr Pearse Keane, NIHR Clinician Scientist at the UCL Institute of Ophthalmology and consultant ophthalmologist at Moorfields Eye Hospital NHS Foundation Trust said: “The number of eye scans we’re performing is growing at a pace much faster than human experts are able to interpret them. There is a risk that this may cause delays in the diagnosis and treatment of sight-threatening diseases, which can be devastating for patients.”

“The AI technology we’re developing is designed to prioritise patients who need to be seen and treated urgently by a doctor or eye care professional. If we can diagnose and treat eye conditions early, it gives us the best chance of saving people’s sight. With further research it could lead to greater consistency and quality of care for patients with eye problems in the future.”

The study, launched in 2016, brought together leading NHS eye health professionals and scientists from UCL and the National Institute for Health Research (NIHR) with some of the UK’s top technologists at DeepMind to investigate whether AI technology could help improve the care of patients with sight-threatening diseases, such as age-related macular degeneration and diabetic eye disease.

Using two types of neural network – mathematical systems for identifying patterns in images or data – the AI system quickly learnt to identify 10 features of eye disease from highly complex optical coherence tomography (OCT) scans. The system was then able to recommend a referral decision based on the most urgent conditions detected.

To establish whether the AI system was making correct referrals, clinicians also viewed the same OCT scans and made their own referral decisions. The study concluded that AI was able to make the right referral recommendation more than 94% of the time, matching the performance of expert clinicians.

The AI has been developed with two unique features which maximise its potential use in eye care. Firstly, the system can provide information that helps explain to eye care professionals how it arrives at its recommendations. This information includes visuals of the features of eye disease it has identified on the OCT scan and the level of confidence the system has in its recommendations, in the form of a percentage. This functionality is crucial in helping clinicians scrutinise the technology’s recommendations and check its accuracy before deciding the type of care and treatment a patient receives.

Secondly, the AI system can be easily applied to different types of eye scanner, not just the specific model on which it was trained. This could significantly increase the number of people who benefit from this technology and future-proof it, so it can still be used even as OCT scanners are upgraded or replaced over time.

The next step is for the research to go through clinical trials to explore how this technology might improve patient care in practice, and regulatory approval before it can be used in hospitals and other clinical settings.

If clinical trials are successful in demonstrating that the technology can be used safely and effectively, Moorfields will be able to use an eventual, regulatory-approved product for free, across all 30 of their UK hospitals and community clinics, for an initial period of five years.

The work that has gone into this project will also help accelerate wider NHS research for many years to come. For example, DeepMind has invested significant resources to clean, curate and label Moorfields’ de-identified research dataset to create one of the most advanced eye research databases in the world.

Moorfields owns this database as a non-commercial public asset, which is already forming the basis of nine separate medical research studies. In addition, Moorfields can also use DeepMind’s trained AI model for future non-commercial research efforts, which could help advance medical research even further.

Mustafa Suleyman, Co-founder and Head of Applied AI at DeepMind Health, said: “We set up DeepMind Health because we believe artificial intelligence can help solve some of society’s biggest health challenges, like avoidable sight loss, which affects millions of people across the globe. These incredibly exciting results take us one step closer to that goal and could, in time, transform the diagnosis, treatment and management of patients with sight threatening eye conditions, not just at Moorfields, but around the world.”

Professor Sir Peng Tee Khaw, director of the NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology said: “The results of this pioneering research with DeepMind are very exciting and demonstrate the potential sight-saving impact AI could have for patients. I am in no doubt that AI has a vital role to play in the future of healthcare, particularly when it comes to training and helping medical professionals so that patients benefit from vital treatment earlier than might previously have been possible. This shows the transformative research than can be carried out in the UK combining world leading industry and NIHR/NHS hospital/university partnerships.”

Matt Hancock, Health and Social Care Secretary, said: “This is hugely exciting and exactly the type of technology which will benefit the NHS in the long term and improve patient care – that’s why we fund over a billion pounds a year in health research as part of our long term plan for the NHS.”

Here’s a link to and a citation for the study,

Clinically applicable deep learning for diagnosis and referral in retinal disease by Jeffrey De Fauw, Joseph R. Ledsam, Bernardino Romera-Paredes, Stanislav Nikolov, Nenad Tomasev, Sam Blackwell, Harry Askham, Xavier Glorot, Brendan O’Donoghue, Daniel Visentin, George van den Driessche, Balaji Lakshminarayanan, Clemens Meyer, Faith Mackinder, Simon Bouton, Kareem Ayoub, Reena Chopra, Dominic King, Alan Karthikesalingam, Cían O. Hughes, Rosalind Raine, Julian Hughes, Dawn A. Sim, Catherine Egan, Adnan Tufail, Hugh Montgomery, Demis Hassabis, Geraint Rees, Trevor Back, Peng T. Khaw, Mustafa Suleyman, Julien Cornebise, Pearse A. Keane, & Olaf Ronneberger. Nature Medicine (2018) DOI: https://doi.org/10.1038/s41591-018-0107-6 Published 13 August 2018

This paper is behind a paywall.

And now, Melissa Locker’s August 15, 2018 article for Fast Company (Note: Links have been removed),

In a paper published in Nature Medicine on Monday, Google’s DeepMind subsidiary, UCL, and researchers at Moorfields Eye Hospital showed off their new AI system. The researchers used deep learning to create algorithm-driven software that can identify common patterns in data culled from dozens of common eye diseases from 3D scans. The result is an AI that can identify more than 50 diseases with incredible accuracy and can then refer patients to a specialist. Even more important, though, is that the AI can explain why a diagnosis was made, indicating which part of the scan prompted the outcome. It’s an important step in both medicine and in making AIs slightly more human

The editor or writer has even highlighted the sentence about the system’s accuracy—not just good but incredible!

I will be publishing something soon [my August 21, 2018 posting] which highlights some of the questions one might want to ask about AI and medicine before diving headfirst into this brave new world of medicine.

China is world leader in nanotechnology and in other fields too?

State of Chinese nanoscience/nanotechnology

China claims to be the world leader in the field in a white paper announced in an August 29, 2017 Springer Nature press release,

Springer Nature, the National Center for Nanoscience and Technology, China and the National Science Library of the Chinese Academy of Sciences (CAS) released in both Chinese and English a white paper entitled “Small Science in Big China: An overview of the state of Chinese nanoscience and technology” at NanoChina 2017, an international conference on nanoscience and technology held August 28 and 29 in Beijing. The white paper looks at the rapid growth of China’s nanoscience research into its current role as the world’s leader [emphasis mine], examines China’s strengths and challenges, and makes some suggestions for how its contribution to the field can continue to thrive.

The white paper points out that China has become a strong contributor to nanoscience research in the world, and is a powerhouse of nanotechnology R&D. Some of China’s basic research is leading the world. China’s applied nanoscience research and the industrialization of nanotechnologies have also begun to take shape. These achievements are largely due to China’s strong investment in nanoscience and technology. China’s nanoscience research is also moving from quantitative increase to quality improvement and innovation, with greater emphasis on the applications of nanotechnologies.

“China took an initial step into nanoscience research some twenty years ago, and has since grown its commitment at an unprecedented rate, as it has for scientific research as a whole. Such a growth is reflected both in research quantity and, importantly, in quality. Therefore, I regard nanoscience as a window through which to observe the development of Chinese science, and through which we could analyze how that rapid growth has happened. Further, the experience China has gained in developing nanoscience and related technologies is a valuable resource for the other countries and other fields of research to dig deep into and draw on,” said Arnout Jacobs, President, Greater China, Springer Nature.

The white paper explores at China’s research output relative to the rest of the world in terms of research paper output, research contribution contained in the Nano database, and finally patents, providing insight into China’s strengths and expertise in nano research. The white paper also presents the results of a survey of experts from the community discussing the outlook for and challenges to the future of China’s nanoscience research.

China nano research output: strong rise in quantity and quality

In 1997, around 13,000 nanoscience-related papers were published globally. By 2016, this number had risen to more than 154,000 nano-related research papers. This corresponds to a compound annual growth rate of 14% per annum, almost four times the growth in publications across all areas of research of 3.7%. Over the same period of time, the nano-related output from China grew from 820 papers in 1997 to over 52,000 papers in 2016, a compound annual growth rate of 24%.

China’s contribution to the global total has been growing steadily. In 1997, Chinese researchers co-authored just 6% of the nano-related papers contained in the Science Citation Index (SCI). By 2010, this grew to match the output of the United States. They now contribute over a third of the world’s total nanoscience output — almost twice that of the United States.

Additionally, China’s share of the most cited nanoscience papers has kept increasing year on year, with a compound annual growth rate of 22% — more than three times the global rate. It overtook the United States in 2014 and its contribution is now many times greater than that of any other country in the world, manifesting an impressive progression in both quantity and quality.

The rapid growth of nanoscience in China has been enabled by consistent and strong financial support from the Chinese government. As early as 1990, the State Science and Technology Committee, the predecessor of the Ministry of Science and Technology (MOST), launched the Climbing Up project on nanomaterial science. During the 1990s, the National Natural Science Foundation of China (NSFC) also funded nearly 1,000 small-scale projects in nanoscience. In the National Guideline on Medium- and Long-Term Program for Science and Technology Development (for 2006−2020) issued in early 2006 by the Chinese central government, nanoscience was identified as one of four areas of basic research and received the largest proportion of research budget out of the four areas. The brain boomerang, with more and more foreign-trained Chinese researchers returning from overseas, is another contributor to China’s rapid rise in nanoscience.

The white paper clarifies the role of Chinese institutions, including CAS, in driving China’s rise to become the world’s leader in nanoscience. Currently, CAS is the world’s largest producer of high impact nano research, contributing more than twice as many papers in the 1% most-cited nanoscience literature than its closest competitors. In addition to CAS, five other Chinese institutions are ranked among the global top 20 in terms of output of top cited 1% nanoscience papers — Tsinghua University, Fudan University, Zhejiang University, University of Science and Technology of China and Peking University.

Nano database reveals advantages and focus of China’s nano research

The Nano database (http://nano.nature.com) is a comprehensive platform that has been recently developed by Nature Research – part of Springer Nature – which contains nanoscience-related papers published in 167 peer-reviewed journals including Advanced Materials, Nano Letters, Nature, Science and more. Analysis of the Nano database of nanomaterial-containing articles published in top 30 journals during 2014–2016 shows that Chinese scientists explore a wide range of nanomaterials, the five most common of which are nanostructured materials, nanoparticles, nanosheets, nanodevices and nanoporous materials.

In terms of the research of applications, China has a clear leading edge in catalysis research, which is the most popular area of the country’s quality nanoscience papers. Chinese nano researchers also contributed significantly to nanomedicine and energy-related applications. China is relatively weaker in nanomaterials for electronics applications, compared to other research powerhouses, but robotics and lasers are emerging applications areas of nanoscience in China, and nanoscience papers addressing photonics and data storage applications also see strong growth in China. Over 80% of research from China listed in the database explicitly mentions applications of the nanostructures and nanomaterials described, notably higher than from most other leading nations such as the United States, Germany, the UK, Japan and France.

Nano also reveals the extent of China’s international collaborations in nano research. China has seen the percentage of its internationally collaborated papers increasing from 36% in 2014 to 44% in 2016. This level of international collaboration, similar to that of South Korea, is still much lower than that of the western countries, and the rate of growth is also not as fast as those in the United States, France and Germany.

The United States is China’s biggest international collaborator, contributing to 55% of China’s internationally collaborated papers on nanoscience that are included in the top 30 journals in the Nano database. Germany, Australia and Japan follow in a descending order as China’s collaborators on nano-related quality papers.

China’s patent output: topping the world, mostly applied domestically

Analysis of the Derwent Innovation Index (DII) database of Clarivate Analytics shows that China’s accumulative total number of patent applications for the past 20 years, amounting to 209,344 applications, or 45% of the global total, is more than twice as many as that of the United States, the second largest contributor to nano-related patents. China surpassed the United States and ranked the top in the world since 2008.

Five Chinese institutions, including the CAS, Zhejiang University, Tsinghua University, Hon Hai Precision Industry Co., Ltd. and Tianjin University can be found among the global top 10 institutional contributors to nano-related patent applications. CAS has been at the top of the global rankings since 2008, with a total of 11,218 patent applications for the past 20 years. Interestingly, outside of China, most of the other big institutional contributors among the top 10 are commercial enterprises, while in China, research or academic institutions are leading in patent applications.

However, the number of nano-related patents China applied overseas is still very low, accounting for only 2.61% of its total patent applications for the last 20 years cumulatively, whereas the proportion in the United States is nearly 50%. In some European countries, including the UK and France, more than 70% of patent applications are filed overseas.

China has high numbers of patent applications in several popular technical areas for nanotechnology use, and is strongest in patents for polymer compositions and macromolecular compounds. In comparison, nano-related patent applications in the United States, South Korea and Japan are mainly for electronics or semiconductor devices, with the United States leading the world in the cumulative number of patents for semiconductor devices.

Outlook, opportunities and challenges

The white paper highlights that the rapid rise of China’s research output and patent applications has painted a rosy picture for the development of Chinese nanoscience, and in both the traditionally strong subjects and newly emerging areas, Chinese nanoscience shows great potential.

Several interviewed experts in the survey identify catalysis and catalytic nanomaterials as the most promising nanoscience area for China. The use of nanotechnology in the energy and medical sectors was also considered very promising.

Some of the interviewed experts commented that the industrial impact of China’s nanotechnology is limited and there is still a gap between nanoscience research and the industrialization of nanotechnologies. Therefore, they recommended that the government invest more in applied research to drive the translation of nanoscience research and find ways to encourage enterprises to invest more in R&D.

As more and more young scientists enter the field, the competition for research funding is becoming more intense. However, this increasing competition for funding was not found to concern most interviewed young scientists, rather, they emphasized that the soft environment is more important. They recommended establishing channels that allow the suggestions or creative ideas of the young to be heard. Also, some interviewed young researchers commented that they felt that the current evaluation system was geared towards past achievements or favoured overseas experience, and recommended the development of an improved talent selection mechanism to ensure a sustainable growth of China’s nanoscience.

I have taken a look at the white paper and found it to be well written. It also provides a brief but thorough history of nanotechnology/nanoscience even adding a bit of historical information that was new to me. As for the rest of the white paper, it relies on bibliometrics (number of published papers and number of citations) and number of patents filed to lay the groundwork for claiming Chinese leadership in nanotechnology. As I’ve stated many times before, these are problematic measures but as far as I can determine they are almost the only ones we have. Frankly, as a Canadian, it doesn’t much matter to me since Canada no matter how you slice or dice it is always in a lower tier relative to science leadership in major fields. It’s the Americans who might feel inclined to debate leadership with regard to nanotechnology and other major fields and I leave it to to US commentators to take up the cudgels should they be inclined. The big bonuses here are the history, the glimpse into the Chinese perspective on the field of nanotechnology/nanoscience, and the analysis of weaknesses and strengths.

Coming up fast on Google and Amazon

A November 16, 2017 article by Christina Bonnington for Slate explores the possibility that a Chinese tech giant, Baidu,  will provide Google and Amazon serious competition in their quests to dominate world markets (Note: Links have been removed,

raven_h
The company took a playful approach to the form—but it has functional reasons for the design, too. Baidu

 

One of the most interesting companies in tech right now isn’t based in Palo Alto, or San Francisco, or Seattle. Baidu, a Chinese company with headquarters in Beijing, is taking on America’s biggest and most innovative tech titans—with style.

Baidu, a titan in its own right, leapt onto the scene as a competitor to Google in the search engine space. Since then, the company, largely underappreciated here in the U.S., has focused on beefing up its artificial intelligence efforts. Former AI chief Andrew Ng, upon leaving the company in March, credited Baidu’s CEO Robin Li on being one of the first technology leaders to fully appreciate the value of deep learning. Baidu now has a 1,300 person AI group, and that investment in AI has helped the company catch up to older, more established companies like Google and Amazon—both in emerging spaces, such as autonomous vehicles, and in consumer tech, as its latest announcement shows.

On Thursday [November 16, 2017], Baidu debuted its entrants to the popular virtual assistant space: a connected speaker and two robots. Baidu aims for the speaker to compete against options such as Amazon’s Echo line, Google Home, and Apple HomePod. Inside, the $256 device will utilize Baidu’s DuerOS conversational artificial intelligence platform, which is already used in more than 100 different smart home brands’ products. DuerOS will let you use your voice to do things like ask the speaker for information, play music, or hail a cab. Called the Raven H, the speaker includes high-end audio components from Tymphany and a unique design jointly created by acquired startup Raven Tech and Swedish consumer electronics company Teenage Engineering.

While the focus is on exciting new technology products from Baidu, the subtext, such as it is, suggests US companies had best keep an eye on its Chinese competitor(s).

Dutch/Chinese partnership to produce nanoparticles at the touch of a button

Now back to China and nanotechnology leadership and the production of nanoparticles. This announcement was made in a November 17, 2017 news item on Azonano,

Delft University of Technology [Netherlands] spin-off VSPARTICLE enters the booming Chinese market with a radical technology that allows researchers to produce nanoparticles at the push of a button. VSPARTICLE’s nanoparticle generator uses atoms, the worlds’ smallest building blocks, to provide a controllable source of nanoparticles. The start-up from Delft signed a distribution agreement with Bio-Sun to make their VSP-G1 nanoparticle generator available in China.

A November 16, 2017 VSPARTICLE press release, which originated the news item,

“We are honoured to cooperate with VSPARTICLE and bring the innovative VSP-G1 nanoparticle generator into the Chinese market. The VSP-G1 will create new possibilities for researchers in catalysis, aerosol, healthcare and electronics,” says Yinghui Cai, CEO of Bio-Sun.

With an exponential growth in nanoparticle research in the last decade, China is one of the leading countries in the field of nanotechnology and its applications. Vincent Laban, CFO of VSPARTICLE, explains: “Due to its immense investments in IOT, sensors, semiconductor technology, renewable energy and healthcare applications, China will eventually become one of our biggest markets. The collaboration with Bio-Sun offers a valuable opportunity to enter the Chinese market at exactly the right time.”

NANOPARTICLES ARE THE BUILDING BLOCKS OF THE FUTURE

Increasingly, scientists are focusing on nanoparticles as a key technology in enabling the transition to a sustainable future. Nanoparticles are used to make new types of sensors and smart electronics; provide new imaging and treatment possibilities in healthcare; and reduce harmful waste in chemical processes.

CURRENT RESEARCH TOOLKIT LACKS A FAST WAY FOR MAKING SPECIFIC BUILDING BLOCKS

With the latest tools in nanotechnology, researchers are exploring the possibilities of building novel materials. This is, however, a trial-and-error method. Getting the right nanoparticles often is a slow struggle, as most production methods take a substantial amount of effort and time to develop.

VSPARTICLE’S VSP-G1 NANOPARTICLE GENERATOR

With the VSP-G1 nanoparticle generator, VSPARTICLE makes the production of nanoparticles as easy as pushing a button. . Easy and fast iterations enable researchers to fast forward their research cycle, and verify their hypotheses.

VSPARTICLE

Born out of the research labs of Delft University of Technology, with over 20 years of experience in the synthesis of aerosol, VSPARTICLE believes there is a whole new world of possibilities and materials at the nanoscale. The company was founded in 2014 and has an international sales network in Europe, Japan and China.

BIO-SUN

Bio-Sun was founded in Beijing in 2010 and is a leader in promoting nanotechnology and biotechnology instruments in China. It serves many renowned customers in life science, drug discovery and material science. Bio-Sun has four branch offices in Qingdao, Shanghai, Guangzhou and Wuhan City, and a nationwide sale network.

That’s all folks!

University of British Columbia (Canada) boards the Coursera train

The last time I featured an online education story was in my Aug. 9, 2011 posting about Stanford University and a free, Artificial Intelligence online course. It was a hugely successful effort and seems to have, at least partially, inspired a whole new institutional approach to offering education.

Universities still want to make money but instead of charging for the courses, they’ll be charging for the certification in these new online education ventures. That’s the theory behind Coursera, founded by Daphne Koller and Andrew Ng at Stanford University (California).

Today, Sept. 19 2012,  Coursera announced that the number of participating educational institutions has doubled. From the Sept. 19, 2012 article by Anya Kamenetz for Fast Company,

Having already teamed up with more colleges than any of its rivals, Coursera adds 17 new global universities to its roster.

Since its debut earlier this year, 1.3 million people have signed up for a free six- to ten-week Coursera class, which includes videos, exercises, embedded assessment and a social component delivered through message boards.

Although still exploring business models, the venture-funded company plans to eventually make money through certifications (a path competitor Udacity is already pursuing). The addition of these new partners will give Coursera an advantage in what’s become an increasingly crowded online education market.

Kamenetz’s article provides more detail about Coursera’s competitors and course offerings. I’m going to concentrate on one of the new universities to team up with the company, the University of British Columbia (from my home province). From the University of British Columbia (UBC) Sept. 19, 2012 media release,

The University of British Columbia is joining forces with the U.S.-based company Coursera to provide high quality, non-credit courses free of charge to a worldwide audience – bringing the university’s expertise within reach of anyone with Internet access.

Starting spring 2013, UBC will pilot three non-credit courses taught by renowned UBC faculty and researchers through Coursera’s online learning platform.

“Our partnership with Coursera will enable us to reach people around the world, and to evaluate an exciting new teaching and learning technology,” says Simon Peacock, Dean of the Faculty of Science, where two of the three UBC Coursera courses will be housed. “Ultimately, I believe all UBC students will benefit from our exploration of this rapidly evolving online space.”

UBC’s Coursera offerings are “Useful Genetics” with Prof. Rosie Redfield [emphasis mine], “Computer Science Problem Design” with Prof. Gregor Kiczales and “Climate Literacy: Navigating Climate Conversations” with Sarah Burch and Tom-Pierre Frappé-Sénéclauze, instructors for the UBC Continuing Studies Centre for Sustainability.

Coursera courses typically consist of videos or voice-over PowerPoint presentations, with student-led discussion forums, interactive activities, quizzes and assignments set at regular intervals.

(Rosie Redfield has been mentioned here before in the context of the ‘arsenic life’ controversy in a Dec. 8, 2010 posting where I apologized for having gotten caught up in the excitement and discuss the controversy at some length.)

Coursera‘s offerings are heavily weighted towards the sciences and mathematics but those are more easily quantifiable than the humanities and I imagine that makes them easier to mark. I understand from Kamenetz’s article, Coursera is testing a peer grading scheme. The website is easy to navigate as is signing up for a course. I do have a couple of  provisos. (1)  I was not able to find out the minimum technical requirement for a potential student’s computer. (2) At this point, they are offering certificates of completion, nothing else. You’re not going to be getting a degree or other professional certification from Stanford or Brown or UBC or any of the others.

On another note, I have a mild quibble with the UBC media release,

• UBC is building upon its leadership in continuing and distance education to enhance the student learning experience. The Coursera partnership will provide evidence-based findings for UBC to design and support quality learning interactions for online, face-to-face and other classroom delivery modes.

I’m not sure I’d call ‘jumping on the train’ with a bunch of other institutions leadership. As for the plan to extract data and mine the Coursera relationship so UBC can design and offer competitive (?) programmes in future, I think that must have been an interesting negotiation. As well, I appreciate the importance of building on someone else’s work as UBC is planning but I’m not sure I’d call that leadership either.