Tag Archives: brain drain

Vector Institute and Canada’s artificial intelligence sector

On the heels of the March 22, 2017 federal budget announcement of $125M for a Pan-Canadian Artificial Intelligence Strategy, the University of Toronto (U of T) has announced the inception of the Vector Institute for Artificial Intelligence in a March 28, 2017 news release by Jennifer Robinson (Note: Links have been removed),

A team of globally renowned researchers at the University of Toronto is driving the planning of a new institute staking Toronto’s and Canada’s claim as the global leader in AI.

Geoffrey Hinton, a University Professor Emeritus in computer science at U of T and vice-president engineering fellow at Google, will serve as the chief scientific adviser of the newly created Vector Institute based in downtown Toronto.

“The University of Toronto has long been considered a global leader in artificial intelligence research,” said U of T President Meric Gertler. “It’s wonderful to see that expertise act as an anchor to bring together researchers, government and private sector actors through the Vector Institute, enabling them to aim even higher in leading advancements in this fast-growing, critical field.”

As part of the Government of Canada’s Pan-Canadian Artificial Intelligence Strategy, Vector will share $125 million in federal funding with fellow institutes in Montreal and Edmonton. All three will conduct research and secure talent to cement Canada’s position as a world leader in AI.

In addition, Vector is expected to receive funding from the Province of Ontario and more than 30 top Canadian and global companies eager to tap this pool of talent to grow their businesses. The institute will also work closely with other Ontario universities with AI talent.

(See my March 24, 2017 posting; scroll down about 25% for the science part, including the Pan-Canadian Artificial Intelligence Strategy of the budget.)

Not obvious in last week’s coverage of the Pan-Canadian Artificial Intelligence Strategy is that the much lauded Hinton has been living in the US and working for Google. These latest announcements (Pan-Canadian AI Strategy and Vector Institute) mean that he’s moving back.

A March 28, 2017 article by Kate Allen for TorontoStar.com provides more details about the Vector Institute, Hinton, and the Canadian ‘brain drain’ as it applies to artificial intelligence, (Note:  A link has been removed)

Toronto will host a new institute devoted to artificial intelligence, a major gambit to bolster a field of research pioneered in Canada but consistently drained of talent by major U.S. technology companies like Google, Facebook and Microsoft.

The Vector Institute, an independent non-profit affiliated with the University of Toronto, will hire about 25 new faculty and research scientists. It will be backed by more than $150 million in public and corporate funding in an unusual hybridization of pure research and business-minded commercial goals.

The province will spend $50 million over five years, while the federal government, which announced a $125-million Pan-Canadian Artificial Intelligence Strategy in last week’s budget, is providing at least $40 million, backers say. More than two dozen companies have committed millions more over 10 years, including $5 million each from sponsors including Google, Air Canada, Loblaws, and Canada’s five biggest banks [Bank of Montreal (BMO). Canadian Imperial Bank of Commerce ({CIBC} President’s Choice Financial},  Royal Bank of Canada (RBC), Scotiabank (Tangerine), Toronto-Dominion Bank (TD Canada Trust)].

The mode of artificial intelligence that the Vector Institute will focus on, deep learning, has seen remarkable results in recent years, particularly in image and speech recognition. Geoffrey Hinton, considered the “godfather” of deep learning for the breakthroughs he made while a professor at U of T, has worked for Google since 2013 in California and Toronto.

Hinton will move back to Canada to lead a research team based at the tech giant’s Toronto offices and act as chief scientific adviser of the new institute.

Researchers trained in Canadian artificial intelligence labs fill the ranks of major technology companies, working on tools like instant language translation, facial recognition, and recommendation services. Academic institutions and startups in Toronto, Waterloo, Montreal and Edmonton boast leaders in the field, but other researchers have left for U.S. universities and corporate labs.

The goals of the Vector Institute are to retain, repatriate and attract AI talent, to create more trained experts, and to feed that expertise into existing Canadian companies and startups.

Hospitals are expected to be a major partner, since health care is an intriguing application for AI. Last month, researchers from Stanford University announced they had trained a deep learning algorithm to identify potentially cancerous skin lesions with accuracy comparable to human dermatologists. The Toronto company Deep Genomics is using deep learning to read genomes and identify mutations that may lead to disease, among other things.

Intelligent algorithms can also be applied to tasks that might seem less virtuous, like reading private data to better target advertising. Zemel [Richard Zemel, the institute’s research director and a professor of computer science at U of T] says the centre is creating an ethics working group [emphasis mine] and maintaining ties with organizations that promote fairness and transparency in machine learning. As for privacy concerns, “that’s something we are well aware of. We don’t have a well-formed policy yet but we will fairly soon.”

The institute’s annual funding pales in comparison to the revenues of the American tech giants, which are measured in tens of billions. The risk the institute’s backers are taking is simply creating an even more robust machine learning PhD mill for the U.S.

“They obviously won’t all stay in Canada, but Toronto industry is very keen to get them,” Hinton said. “I think Trump might help there.” Two researchers on Hinton’s new Toronto-based team are Iranian, one of the countries targeted by U.S. President Donald Trump’s travel bans.

Ethics do seem to be a bit of an afterthought. Presumably the Vector Institute’s ‘ethics working group’ won’t include any regular folks. Is there any thought to what the rest of us think about these developments? As there will also be some collaboration with other proposed AI institutes including ones at the University of Montreal (Université de Montréal) and the University of Alberta (Kate McGillivray’s article coming up shortly mentions them), might the ethics group be centered in either Edmonton or Montreal? Interestingly, two Canadians (Timothy Caulfield at the University of Alberta and Eric Racine at Université de Montréa) testified at the US Commission for the Study of Bioethical Issues Feb. 10 – 11, 2014 meeting, the Brain research, ethics, and nanotechnology. Still speculating here but I imagine Caulfield and/or Racine could be persuaded to extend their expertise in ethics and the human brain to AI and its neural networks.

Getting back to the topic at hand the ‘AI sceneCanada’, Allen’s article is worth reading in its entirety if you have the time.

Kate McGillivray’s March 29, 2017 article for the Canadian Broadcasting Corporation’s (CBC) news online provides more details about the Canadian AI situation and the new strategies,

With artificial intelligence set to transform our world, a new institute is putting Toronto to the front of the line to lead the charge.

The Vector Institute for Artificial Intelligence, made possible by funding from the federal government revealed in the 2017 budget, will move into new digs in the MaRS Discovery District by the end of the year.

Vector’s funding comes partially from a $125 million investment announced in last Wednesday’s federal budget to launch a pan-Canadian artificial intelligence strategy, with similar institutes being established in Montreal and Edmonton.

“[A.I.] cuts across pretty well every sector of the economy,” said Dr. Alan Bernstein, CEO and president of the Canadian Institute for Advanced Research, the organization tasked with administering the federal program.

“Silicon Valley and England and other places really jumped on it, so we kind of lost the lead a little bit. I think the Canadian federal government has now realized that,” he said.

Stopping up the brain drain

Critical to the strategy’s success is building a homegrown base of A.I. experts and innovators — a problem in the last decade, despite pioneering work on so-called “Deep Learning” by Canadian scholars such as Yoshua Bengio and Geoffrey Hinton, a former University of Toronto professor who will now serve as Vector’s chief scientific advisor.

With few university faculty positions in Canada and with many innovative companies headquartered elsewhere, it has been tough to keep the few graduates specializing in A.I. in town.

“We were paying to educate people and shipping them south,” explained Ed Clark, chair of the Vector Institute and business advisor to Ontario Premier Kathleen Wynne.

The existence of that “fantastic science” will lean heavily on how much buy-in Vector and Canada’s other two A.I. centres get.

Toronto’s portion of the $125 million is a “great start,” said Bernstein, but taken alone, “it’s not enough money.”

“My estimate of the right amount of money to make a difference is a half a billion or so, and I think we will get there,” he said.

Jessica Murphy’s March 29, 2017 article for the British Broadcasting Corporation’s (BBC) news online offers some intriguing detail about the Canadian AI scene,

Canadian researchers have been behind some recent major breakthroughs in artificial intelligence. Now, the country is betting on becoming a big player in one of the hottest fields in technology, with help from the likes of Google and RBC [Royal Bank of Canada].

In an unassuming building on the University of Toronto’s downtown campus, Geoff Hinton laboured for years on the “lunatic fringe” of academia and artificial intelligence, pursuing research in an area of AI called neural networks.

Also known as “deep learning”, neural networks are computer programs that learn in similar way to human brains. The field showed early promise in the 1980s, but the tech sector turned its attention to other AI methods after that promise seemed slow to develop.

“The approaches that I thought were silly were in the ascendancy and the approach that I thought was the right approach was regarded as silly,” says the British-born [emphasis mine] professor, who splits his time between the university and Google, where he is a vice-president of engineering fellow.

Neural networks are used by the likes of Netflix to recommend what you should binge watch and smartphones with voice assistance tools. Google DeepMind’s AlphaGo AI used them to win against a human in the ancient game of Go in 2016.

Foteini Agrafioti, who heads up the new RBC Research in Machine Learning lab at the University of Toronto, said those recent innovations made AI attractive to researchers and the tech industry.

“Anything that’s powering Google’s engines right now is powered by deep learning,” she says.

Developments in the field helped jumpstart innovation and paved the way for the technology’s commercialisation. They also captured the attention of Google, IBM and Microsoft, and kicked off a hiring race in the field.

The renewed focus on neural networks has boosted the careers of early Canadian AI machine learning pioneers like Hinton, the University of Montreal’s Yoshua Bengio, and University of Alberta’s Richard Sutton.

Money from big tech is coming north, along with investments by domestic corporations like banking multinational RBC and auto parts giant Magna, and millions of dollars in government funding.

Former banking executive Ed Clark will head the institute, and says the goal is to make Toronto, which has the largest concentration of AI-related industries in Canada, one of the top five places in the world for AI innovation and business.

The founders also want it to serve as a magnet and retention tool for top talent aggressively head-hunted by US firms.

Clark says they want to “wake up” Canadian industry to the possibilities of AI, which is expected to have a massive impact on fields like healthcare, banking, manufacturing and transportation.

Google invested C$4.5m (US$3.4m/£2.7m) last November [2016] in the University of Montreal’s Montreal Institute for Learning Algorithms.

Microsoft is funding a Montreal startup, Element AI. The Seattle-based company also announced it would acquire Montreal-based Maluuba and help fund AI research at the University of Montreal and McGill University.

Thomson Reuters and General Motors both recently moved AI labs to Toronto.

RBC is also investing in the future of AI in Canada, including opening a machine learning lab headed by Agrafioti, co-funding a program to bring global AI talent and entrepreneurs to Toronto, and collaborating with Sutton and the University of Alberta’s Machine Intelligence Institute.

Canadian tech also sees the travel uncertainty created by the Trump administration in the US as making Canada more attractive to foreign talent. (One of Clark’s the selling points is that Toronto as an “open and diverse” city).

This may reverse the ‘brain drain’ but it appears Canada’s role as a ‘branch plant economy’ for foreign (usually US) companies could become an important discussion once more. From the ‘Foreign ownership of companies of Canada’ Wikipedia entry (Note: Links have been removed),

Historically, foreign ownership was a political issue in Canada in the late 1960s and early 1970s, when it was believed by some that U.S. investment had reached new heights (though its levels had actually remained stable for decades), and then in the 1980s, during debates over the Free Trade Agreement.

But the situation has changed, since in the interim period Canada itself became a major investor and owner of foreign corporations. Since the 1980s, Canada’s levels of investment and ownership in foreign companies have been larger than foreign investment and ownership in Canada. In some smaller countries, such as Montenegro, Canadian investment is sizable enough to make up a major portion of the economy. In Northern Ireland, for example, Canada is the largest foreign investor. By becoming foreign owners themselves, Canadians have become far less politically concerned about investment within Canada.

Of note is that Canada’s largest companies by value, and largest employers, tend to be foreign-owned in a way that is more typical of a developing nation than a G8 member. The best example is the automotive sector, one of Canada’s most important industries. It is dominated by American, German, and Japanese giants. Although this situation is not unique to Canada in the global context, it is unique among G-8 nations, and many other relatively small nations also have national automotive companies.

It’s interesting to note that sometimes Canadian companies are the big investors but that doesn’t change our basic position. And, as I’ve noted in other postings (including the March 24, 2017 posting), these government investments in science and technology won’t necessarily lead to a move away from our ‘branch plant economy’ towards an innovative Canada.

You can find out more about the Vector Institute for Artificial Intelligence here.

BTW, I noted that reference to Hinton as ‘British-born’ in the BBC article. He was educated in the UK and subsidized by UK taxpayers (from his Wikipedia entry; Note: Links have been removed),

Hinton was educated at King’s College, Cambridge graduating in 1970, with a Bachelor of Arts in experimental psychology.[1] He continued his study at the University of Edinburgh where he was awarded a PhD in artificial intelligence in 1977 for research supervised by H. Christopher Longuet-Higgins.[3][12]

It seems Canadians are not the only ones to experience  ‘brain drains’.

Finally, I wrote at length about a recent initiative taking place between the University of British Columbia (Vancouver, Canada) and the University of Washington (Seattle, Washington), the Cascadia Urban Analytics Cooperative in a Feb. 28, 2017 posting noting that the initiative is being funded by Microsoft to the tune $1M and is part of a larger cooperative effort between the province of British Columbia and the state of Washington. Artificial intelligence is not the only area where US technology companies are hedging their bets (against Trump’s administration which seems determined to terrify people from crossing US borders) by investing in Canada.

For anyone interested in a little more information about AI in the US and China, there’s today’s (March 31, 2017)earlier posting: China, US, and the race for artificial intelligence research domination.

Chinese science at a transition point: a Nature Publishing Group white paper

China and its pursuit of scientific prowess is a matter of some interest around the world and Nature Publishing Group (owned by Springer) has produced a white paper on the topic.  From a Nov. 26, 2015 Springer press release (also on EurekAlert),

Nature Publishing Group (NPG), part of Springer Nature, today releases Turning Point: Chinese Science in Transition, a White Paper which takes the pulse of China’s scientific research at a critical time in its development. It is the first report of its kind to be undertaken in China by a global publisher, drawing on quantitative and qualitative data NPG has recently gathered through interviewing and surveying more than 1,700 leading Chinese researchers.

As its pace of economic growth slows, China’s stated aim is to move towards a more sustainable knowledge-based economy which will be driven by scientific and technological innovation. But the White Paper notes that average academic impact of Chinese research is not yet matching its growth in output, and lags behind the world average in a number of subject areas in normalized citation impact, one of the indicators of impact from research. The Chinese research environment therefore, like its economy, is at a turning point, and faces some unique challenges that need to be overcome in order to improve the quality and impact of the scientific output that will support sustainable growth.

The press release expands on the theme,

The White Paper starts by focusing on a positive trend in Chinese science. It shows that China’s long-lamented ‘brain drain’ has become a ‘brain boomerang’, with the vast majority of young Chinese scientists planning to return quickly to China after a period overseas: 85% plan to return within 5 years. This trend of faster-returning ‘haigui’ (homing turtles, as they are colloquially referred to in China), reflects the country’s increased standing in global research, and a greater confidence Chinese scientists have in the country’s future. China’s increased efforts to attract, develop and retain talented researchers are also securing greater numbers from abroad.

In order to develop and retain these scientists, the White Paper argues that it is vital to implement policies and funding schemes that better address their needs and concerns. In a bid to better understand these, the White Paper looks into three key stages of research process: funding, conducting and sharing research. It concludes that the picture of the fundamental components of the research ecosystem in China is overwhelmingly positive, but there are still anomalies and barriers that frustrate researchers and thwart progress towards a culture that recognizes and rewards excellence and innovation. …

Commenting on the White Paper, Charlotte Liu, President of Springer Nature in Greater China, said: “Just like China’s economy, Chinese science is at a turning point. The range of proposed suggestions and solutions found in this White Paper are based on our first-hand, wide-ranging study and explicitly address some of the issues our research identifies. They are intended to help China become more successful in this transition period. We believe that if they are refined, detailed and implemented by the key stakeholders associated with the research process, they provide the opportunity for China not just to be seen as a research giant but to establish an entrenched culture of innovation that can establish it as a global science and technology leader.”

The press release also provides a full summary of the report’s findings and recommendations,

1. Funding research

China’s funding system has already made some significant progress towards more rigorously meritocratic assessment, but the surveyed scientists still identified several key areas for improvement. More than 80% of those surveyed said China should devote more funding to basic research. Three quarters believe that funders do not take enough risks in funding research whose potential impact or practical value is unclear. “Take Nash’s game theory as an example … no one saw any commercial value of this purely theoretical study back then … but it has made very significant impacts later on …” said one researcher. Many respondents also want funding bodies to invest more in young scientists, offering them larger and more stable programmes. In terms of funding application processes, two thirds of those surveyed said that fairness and efficiency have improved, largely due to procedures implemented by the NSFC, the leading funding source for Chinese scientists. However there is still room for improvement, particularly with respect to megaproject grants. Moreover, many respondents see excessively rigid regulation of grant spending as a major impediment to scientists’ efficiency and productivity. Around two fifths reported spending more than 20% of their time on funding-related activities.

Key recommendations:

  • Funding bodies can drive profound innovation by funding more basic research.
  • Continued investment in “blue sky” ideas will generate long-term rewards.
  • Funding bodies can improve productivity and derive longer term benefits by investing more in young scientists.
  • Research efficiency can be transformed through increasing funding allowances for human resources.
  • Funding bodies can further strengthen funding efficiency and transparency with more merit-based peer review.
  • Engagement of the broader research community when conceptualizing and awarding megaproject grants can promote fairness in funding allocation and improve return on investment of these projects.
  • Funders can help scientists to be more productive and efficient by minimising administrative hurdles and optimising flexibility in grant spending.
  • Streamlining fund reporting, evaluation and financial audit processes will allow more time for scientists to focus on research itself.

2. Conducting research

In recent decades, more and more young Chinese scientists have started to run their own laboratories and research projects. However, more than three quarters of those surveyed felt they did not receive enough mentoring at an early stage, and young scientists were more likely to feel the mentoring they received was insufficient. This problem is more prevalent for researchers that have not been overseas with a large majority of home grown PhDs (66%), post-docs (72%) and PIs (77%) in China saying they have not received sufficient mentoring. Beyond funding and mentoring, other forms of support are needed, including training for writing papers and grant applications, data management and research project management. NPG’s survey also revealed that the lack of postdoctoral fellows and lab technicians represents a challenge. Experienced postdocs can make a principal investigator’s (PI) time more scalable and can also play a key role in mentoring junior students and staff. In terms of collaboration, almost all of those surveyed agreed that opportunities for collaboration are improving in China, but they still identified several barriers that should be addressed, such as competition for first authorship and tedious administrative procedures. “We over-emphasize the institution of the first author or even the first corresponding author … This is ridiculous and obviously shows the sign of administrative intrusion. This is a barrier rooted in our system,” was one telling comment. In addition, the survey explored the global problem of scientific misconduct. While two fifths of the researchers surveyed thought that the level of misconduct in China is about the same as that abroad, a similar proportion felt that misconduct is a more serious problem in China and the lack of sophistication of ethics training was highlighted by some: “For instance, I had … a student in my lab… [who]used the same graphs and text from a submitted article in another article. He didn’t know that this is not allowed,” said one PI.

Key recommendations:

  • Research institutions could free up senior scientists’ time for hands-on mentoring of young scientists by reducing their administrative workloads.
  • Improved training in writing papers and grant applications is needed to help Chinese scientists compete on the global stage.
  • Expanded training in data management and research project management will increase productivity, efficiency and reproducibility.
  • A promotion of the value institutes place on the positions of lab technicians and post-doctoral fellow, greater compensation for contract based researchers and less emphasis on hiring rules such as quotas for full-time positions would help address shortfalls identified in terms of China’s scientific workforce.
  • By reorienting hiring decisions to focus on research output rather than overseas training experience, institutes can keep more talented scientists in China.
  • Funders and institutes can promote domestic collaboration by considering more nuanced ways of assessing research to ease the competition for first authorship.
  • Chinese authorities can also facilitate international collaboration by removing administrative barriers to healthy academic exchange.
  • Measures to reduce such misconduct in China include systematic training and, when necessary, the setting up of independent investigations that penalize those found violating codes of ethics.

3. Sharing research

Sharing science encompasses disseminating research outcomes with other scientists, together with engaging the wider community, policy makers and business leaders through science communication. But NPG’s survey suggests that Chinese researchers have little enthusiasm for, or even awareness of, the global trend towards openly sharing data. Paper writing is usually the last step in research. The majority of those surveyed reported spending more than one working day per week on paper writing, and some reported spending more than half of their time writing. Language barriers are not the only issue: “In Western countries, they start writing essays early. It’s integrated in their undergraduate education. Or … even since primary school … But this is lacking from our education system.” As the number of papers coming out of China increases, Chinese scientists are aiming higher, with 87% of the surveyed scientists indicating that they are likely to publish relatively fewer papers each year in future, but with the aim of targeting higher profile journals. Making sure there is a level playing field is a major concern: “I feel there is a bias against Chinese authors in publishing. [emphasis mine] Most editors and reviewers are from western countries. It’s not surprising that they will give more time and trust to an article from a famous (western) institute or lab, and they tend to be harsher to an article from a Chinese lab that they never heard of,” one group leader commented. Although Chinese scientists recognize the importance of communicating their research to the wider public, only around half of those surveyed had experience of some type of science communication in the past three years.

Key recommendations:

  • Implementing measures that better encourage researchers to share their data and research would benefit their participation in the global movement towards openly sharing data.
  • Better training in scientific writing for researchers would address the problems they report experiencing when writing papers and communicating research.
  • To address issues with commercial editing services, a global industry-wide accreditation system would help to maintain quality standards.
  • Chinese institutes and funding bodies should encourage researchers to play an active role in improving public understanding of science, by providing support and incentives for excellent science communication.
  • More professional and effective science communication outlets are needed.

While the bias issue is not addressed in the summary, a response can be found in the report,

Invisible barriers?

Chinese scientists share the same anxieties with their counterparts around the world in waiting for responses from journals after submitting their papers. Long response times, especially for high-impact journals,and ambiguous responses from editors and reviewers are common sources of frustration. But some surveyed Chinese PIs also believe that they are treated unfairly by the peer review system of international journals, especially high-impact journals. Editors and reviewers from these journals are perceived as being harsher on papers from Chinese authors based in Chinese institutes. Several journal-specific studies showed a higher rejection rate for papers from China, including many Nature branded journals21.

A study on peer review in the journal Biological Conservation suggests Chinese scientists do face greater difficulties in getting published: papers from China are more likely to get rejected before being sent for review, and are more likely to receive negative reviewer recommendations22. This issue could be due to a relatively lower quality of research submitted to the journal, or less clarity in communication. But some suspect that a bias against Chinese authors is at play. So what can be done to reduce bias and/ or the perception of bias?

Measures to increase the number of Chinese reviewers could be part of the solution. The attitudes of the surveyed PIs towards Chinese reviewers varied. Some preferred Chinese reviewers but others expressed concerns that they might be even harsher on domestic peers due to direct competition. [emphasis mine] Nevertheless, the number of reviewers from China remains small relative to the growing number of high-profile papers published by Chinese scientists. A key problem is that it is often difficult for foreign journals to enlist Chinese scientists as reviewers because they are not familiar with the areas of expertise of potential candidates. A couple of initiatives could help.

First, Chinese institutes can enhance the visibility of their researchers by, for example, creating more accessible English pages on their institutional websites. In this way, other researchers around the
world would be better able to select appropriate Chinese researchers as referees. This would also improve global collaboration opportunities for Chinese researchers.

Second, because non-Chinese typically find Chinese names difficult to pronounce and remember, promotion of  the Open Researcher and Contributor ID (ORCID) in China will be essential. ORCID is unique to each researcher and allows unambiguous identification of researcher records and contributions for the purposes of peer review selection, as well as ultimately individual-focused assessment exercises.

Beyond measures that increase the proportion of Chinese reviewers, bias and/or perception of bias against Chinese researchers must be dealt with by the key implementers of the editorial and peer review process: journals and publishers.  In particular,  these stakeholders must continue to innovate and experiment with the peer review process in consultation with the broader research community to reduce the potential for bias in the process.Peer review models that are double- (authors and peer reviewers) and triple- (+editors) blinded or that are much more open should  be experimented with. [pp. 16-7 print version; pp. 20-21 PDF]

The comment that Chinese reviewers might be “… even harsher on domestic peers due to direct competition” could be made about any peer review process. One of problems inherent in peer review is that your peers are likely to be competitors. Interestingly, the recommendations do not suggest an further examination of publishers and journals investigating bias not only towards Chinese researchers but researchers from other countries where English is not the primary or dominant language. They might then be able to refine their understanding of how bias affects their peer review process.

I am glad to see the recommendation for greater innovation in peer review including blinding and more openness although the onus does seem to be on the Chinese to make changes. You can find the full report here.

I was unaware of Nature’s change of status until reading this May 6, 2015 press release. For anyone else who finds themselves a bit surprised, here’s more about Springer Nature from their LinkedIn page,

Springer Nature is a leading global research, educational and professional publisher, home to an array of respected and trusted brands providing quality content through a range of innovative products and services.

Springer Nature is the world’s largest academic book publisher, publisher of the world’s highest impact journals and a pioneer in the field of open research. The company numbers almost 13,000 staff in over 50 countries and has a turnover of approximately EUR 1.5 billion. Springer Nature was formed in 2015 through the merger of Nature Publishing Group, Palgrave Macmillan, Macmillan Education and Springer Science+Business Media.

There you have it.