AI | James Colliander

Budget 2017, Naylor’s review, and the Mathematical Sciences in Canada

Fri, 21 Apr 2017 19:29:59 +0000

James Colliander, Director of the Pacific Institute for the Mathematical Sciences (PIMS)
Nassif Ghoussoub, Director of the Banff International Research Station (BIRS)
Ian Hambleton, Director of the Fields Institute for Research in Mathematical Sciences (Fields)
Luc Vinet, Directeur du Centre de Recherches Mathématiques (CRM)

The direct funding of research initiatives on artificial intelligence (AI) and quantum computing via Budget 2017, and the release of the report of Canada’s Fundamental Science Review present an opportunity to reflect on the role of mathematical sciences within Canada’s scientific heritage and future, but also on our country’s ways of funding research.

The importance of the mathematical sciences (mathematics, statistics and computer science) is deepening in almost all areas of knowledge. Mathematical sciences provide a conceptual infrastructure underpinning advances in biology, engineering, humanities, medicine, social sciences and beyond. Progress in our understanding in all these fields depends upon advanced research and high-level training in the mathematical sciences.

Canadian Mathematician John Charles Fields, the creator of the Fields medal (often dubbed the Nobel prize for mathematics), also played a key role in the founding of the National Research Council in 1916. Today, Canada is served by a collaborative network of mathematical sciences research institutes: the Centre de Recherches Mathématiques (CRM) in Quebec, the Fields Institute for Research in Mathematical Sciences in Ontario, and the Pacific Institute for the Mathematical Sciences (PIMS) in Western Canada.

The institutes amplify Canada’s capacity for discovery and invention through partnerships that intertwine our nation’s universities with academic and industrial researchers from across the globe. Together, the institutes created Mitacs in 1999, which, under the leadership of Arvind Gupta, became a cornerstone of the government’s effort to link our graduate students (in all disciplines) with industry. This accomplishment was amply recognized in Budget 2017. In 2003, they collaborated with Berkeley’s Mathematical Sciences Research Institute (MSRI) to found the Banff International Research Station (BIRS), a unique North-American research infrastructure on Canadian soil, that provides an environment for creative, synergetic, intense and prolonged interactions between mathematical scientists and investigators in other areas of research.

In 2002, the three institutes committed to provide long-term funding to the Atlantic Association for Research in the Mathematical Sciences (AARMS), a network that plays an important role in the mathematical sciences research activities of the Atlantic region.

In 2003, PIMS collaborated with Berkeley’s Mathematical Sciences Research Institute (MSRI) to found the Banff International Research Station (BIRS), a unique North-American research infrastructure on Canadian soil, that provides an environment for creative, synergetic, intense and prolonged interactions between mathematical scientists and investigators in other areas of research.

The need for leadership to advance the mathematical, computational and statistical understanding of information, the development of data science, and the advent of machine learning, prompted the institutes in 2012 to use their own resources to invest in the creation of CANSSI, the Canadian Statistical Sciences Institute. That NSERC did not have the resources to do so at that time sheds some light on the community’s reaction to how Budget 2017 continues to shut out the Tri-Council.

AI rests on mathematical sciences. Indeed, some of this field’s prominent leaders pursued their foundational research within BIRS, CRM, Fields and PIMS. AI needs further advances in the mathematical sciences to thrive. We celebrate strong support of AI research but the disjointed approach used by government for its substantial investment in the area of deep learning totally missed the opportunity to include and exploit the national resource that BIRS, CRM, Fields, PIMS and CANSSI represent.

This example is but one of many examples that illustrate the importance of some of the excellent recommendations of the report of the Science Review Panel. Indeed, Canada developed world leading expertise and operations in science policy during the era of Pierre Elliot Trudeau. The work of the Senate Special Committee on Science Policy, chaired by Senator Maurice Lamontagne, identified principles to guide Canada’s future governments. The consultation of the scientific community overseen by the panel chaired by David Naylor aligns with these best practices. Direct investments disbursed through political channels, instead of through the Tri-Council’s scientific peer review process, undermine the transparency of research funding programs, and can miss opportunities such as the one we described. Funding allocations to support research should follow consistent and rigorous evaluation processes incorporating independent scientific peer review.

Artificial Intelligence as a Service: Text Analysis

Fri, 03 Jun 2016 19:11:25 +0000

The computing infrastructure as a service offered by Amazon Web Services (AWS) may have originally been conceived as a resource to support their own electronic commerce business but other striking applications emerged. Netflix toppled Blockbuster and transformed the way we consume video atop the AWS infrastructure. Dropbox changed the way we store and share digital resources by building on AWS. My company Crowdmark leverages AWS to store and serve images of student work for evaluation by graders and further analysis. Recently, the Google Cloud Vision API, the IBM Watson Developer Cloud and the Receptiviti.ai API started offering artificial intelligence as a service available for purchase like a utility. These resources may form the foundation for a new era of technological metamorphosis.

Experimenting with Text Analysis

I wondered whether text analysis might generate useful insights for Crowdmark or other applications so I performed some experiments. I ran the texts from inaugural addresses by four presidents of the United States through some text analysis tools I found online. The IBM Watson Tone Analyzer uses “cognitive linguistic analysis methods” to measure the emotional tone in text. The Linguistic Inventory Word Count (LIWC) is a computer text analysis tool developed and psychometrically validated by James Pennebaker. Receptiviti.ai is a Toronto-based startup that offers text analysis as a service based on LIWC and other technology.

The texts

The tools

Screen captures of the results appear below.

Conclusion

The IBM Watson Tone Analyzer results are exposed within an intuitive and interactive user interface. The results do not correspond well with my own emotional response reviewing the videos or reading these speeches. Based on these experiments, I am not convinced that Tone Analyzer will generate useful insights into the emotional characteristics of text. Based on what I observed, there appear to be too few dimensions of emotional tone generated by Tone Analyzer for it to drive improvements to the dialogue between instructors and students. There may be a rich superset of output measurements not exposed in this free demonstration.

The LIWC results exposed through this free sample analysis are a small collection of the many dimensions measured by LIWC. It is not easy to glean insights into the speaker’s personality or their emotional tone based on the reports externalized in this free demo.

Short text descriptions of personality traits of the speaker emerged in the results from Receptiviti.ai. I found the text descriptions interesting but with limited precision in describing the speakers. I’m curious to know whether the personality decription accuracy increases with larger text samples from the same speaker.

Files of various types (text, images) may now be sent to increasingly sophisticated online analysis engines poised and ready to extract data and return insights to the sender. What will your robot assistant read for you tomorrow?

Experiments with IBM Watson Tone Analyzer

IBM Watson Tone Analyzer

Reagan Experiment

As a first experiment, I copied the text from the January 20, 1981 Inaugural Address of President Ronald Reagan (video) and pasted it into the IBM Watson Tone Analyzer. Here are the overview results:

Moving the mouse over portions of the text reveals the results of tonal analysis of the highlighted paragraph.

Tonal dimensions can be highlighted and sentences can be ranked based on tonal strength. For example, here is the sentence ranked highest for Disgust.

Clinton Experiment

As a second experiment, I processed the text from the January 20, 1993 Inaugural Address of President Bill Clinton (video) with Tone Analyzer. Here are the overview results:

Tone Analyzer reports that Clinton’s speech is most dominated by Fear.

Kennedy Experiment

For a third experiment, I chose the text from January 20, 1961 Inaugural Address of President John Kennedy (video):

Eisenhower Experiment

For the final experiment, I processed the text from January 21, 1957 Inaugural Address of President Dwight Eisenhower (video):

Overview Comparisons using IBM Watson Tone Analyzer

Reagan 1980

Clinton 1993

Kennedy 1961

Eisenhower 1957

Experiments with LIWC

LIWC

Reagan via LIWC
Clinton via LIWC
Kennedy via LIWC
Eisenhower via LIWC

Experiments with Receptiviti Personality Insights

Receptiviti.ai

Reagan via Receptiviti.ai
Clinton via Receptiviti.ai
Kennedy via Receptiviti.ai
Eisenhower via Receptiviti.ai