Recording a conversation in secret is not a criminal offence and is not prohibited. As long as the recording is for personal use you don’t need to obtain consent or let the other person know.
The security man in the foyer of No 10 Downing Street asks that you turn off your phone and deposit it in a wooden cubby shelf built into the wall. I sometimes wondered what the odds were that someone might walk out with my phone -- a disaster, obviously.
But it is not difficult to keep your phone as close attention is not paid. (Or, one could enter with more than one phone.) I'm not saying I have ever disobeyed the rules but I know that it is possible.
Of course the No 10 staffers all have their phones, which are necessary for their work throughout the day.
Thus every meeting at the heart of British government is in danger of being surreptitiously but legally recorded.
Dominic Cummings 'has audio recordings of key government conversations', ally claims (Daily Mail)
Dominic Cummings 'has audio recordings of key government conversations' and 'can back up a lot of his claims', ally of the former chief adviser says.
Dominic Cummings kept audio recordings of key conversations, an ally claims
Former chief adviser is locked in an explosive war of words with Boris Johnson.
Whitehall source said officials did not know extent of material Mr Cummings has.
Dominic Cummings kept audio recordings of key conversations in government, an ally claimed last night.
The former chief adviser is locked in an explosive war of words with Boris Johnson after Downing Street accused him of a string of damaging leaks.
No 10 attempted to rubbish his claims on Friday night, saying it was not true that the Prime Minister had discussed ending a leak inquiry after a friend of his fiance Carrie Symonds was identified as the likely suspect.
But an ally of Mr Cummings said the PM's former chief adviser had taken a treasure trove of material with him when he left Downing Street last year, including audio recordings of discussions with senior ministers and officials.
'Dom has stuff on tape,' the ally said. 'They are mad to pick a fight with him because he will be able to back up a lot of his claims.
My own views (consistent, as far as I can tell, with what Steve says in the talk):
1. Evidence for recent warming (~1 degree C) is strong.
2. There exist previous eras of natural (non-anthropogenic) global temperature change of similar magnitude to what is happening now.
3. However, it is plausible that at least part of the recent temperature rise is due to increase of atmospheric CO2 due to human activity.
4. Climate models still have significant uncertainties. While the direct effect of CO2 IR absorption is well understood, second order effects like clouds, distribution of water vapor in the atmosphere, etc. are not under good control. The increase in temperature from a doubling of atmospheric CO2 is still uncertain to a factor of 2-3 and at the low range (e.g., 1.5 degree C) is not catastrophic. The direct effect of CO2 absorption is modest and at the low range (~1 degree C) of current consensus model predictions. Potentially catastrophic outcomes are due to second order effects that are not under good theoretical or computational control.
5. Even if a catastrophic outcome is only a low probability tail risk, it is prudent to explore technologies that reduce greenhouse gas production.
6. A Red Team exercise, properly done, would clarify what is certain and uncertain in climate science.
Simply stating these views can get you attacked by crazy people.
Buy Steve's book for an accessible and fairly non-technical explanation of these points.
WSJ: ... Barack Obama is one of many who have declared an “epistemological crisis,” in which our society is losing its handle on something called truth.
Thus an interesting experiment will be his and other Democrats’ response to a book by Steven Koonin, who was chief scientist of the Obama Energy Department. Mr. Koonin argues not against current climate science but that what the media and politicians and activists say about climate science has drifted so far out of touch with the actual science as to be absurdly, demonstrably false.
This is not an altogether innocent drifting, he points out in a videoconference interview from his home in Cold Spring, N.Y. In 2019 a report by the presidents of the National Academies of Sciences claimed the “magnitude and frequency of certain extreme events are increasing.” The United Nations Intergovernmental Panel on Climate Change, which is deemed to compile the best science, says all such claims should be treated with “low confidence.”
... Mr. Koonin, 69, and I are of one mind on 2018’s U.S. Fourth National Climate Assessment, issued in Donald Trump’s second year, which relied on such overegged worst-case emissions and temperature projections that even climate activists were abashed (a revolt continues to this day). “The report was written more to persuade than to inform,” he says. “It masquerades as objective science but was written as—all right, I’ll use the word—propaganda.”
Mr. Koonin is a Brooklyn-born math whiz and theoretical physicist, a product of New York’s selective Stuyvesant High School. His parents, with less than a year of college between them, nevertheless intuited in 1968 exactly how to handle an unusually talented and motivated youngster: You want to go cross the country to Caltech at age 16? “Whatever you think is right, go ahead,” they told him. “I wanted to know how the world works,” Mr. Koonin says now. “I wanted to do physics since I was 6 years old, when I didn’t know it was called physics.”
He would teach at Caltech for nearly three decades, serving as provost in charge of setting the scientific agenda for one of the country’s premier scientific institutions. Along the way he opened himself to the world beyond the lab. He was recruited at an early age by the Institute for Defense Analyses, a nonprofit group with Pentagon connections, for what he calls “national security summer camp: meeting generals and people in congress, touring installations, getting out on battleships.” The federal government sought “engagement” with the country’s rising scientist elite. It worked.
He joined and eventually chaired JASON, an elite private group that provides classified and unclassified advisory analysis to federal agencies. (The name isn’t an acronym and comes from a character in Greek mythology.) He got involved in the cold-fusion controversy. He arbitrated a debate between private and government teams competing to map the human genome on whether the target error rate should be 1 in 10,000 or whether 1 in 100 was good enough.
He began planting seeds as an institutionalist. He joined the oil giant BP as chief scientist, working for John Browne, now Baron Browne of Madingley, who had redubbed the company “Beyond Petroleum.” Using $500 million of BP’s money, Mr. Koonin created the Energy Biosciences Institute at Berkeley that’s still going strong. Mr. Koonin found his interest in climate science growing, “first of all because it’s wonderful science. It’s the most multidisciplinary thing I know. It goes from the isotopic composition of microfossils in the sea floor all the way through to the regulation of power plants.”
From deeply examining the world’s energy system, he also became convinced that the real climate crisis was a crisis of political and scientific candor. He went to his boss and said, “John, the world isn’t going to be able to reduce emissions enough to make much difference.”
Any reader would benefit from its deft, lucid tour of climate science, the best I’ve seen. His rigorous parsing of the evidence will have you questioning the political class’s compulsion to manufacture certainty where certainty doesn’t exist. You will come to doubt the usefulness of centurylong forecasts claiming to know how 1% shifts in variables will affect a global climate that we don’t understand with anything resembling 1% precision. ...
Note Added from comments:
If you're older like Koonin or myself you can remember a time when climate change was entirely devoid of tribal associations -- it was not in the political domain at all. It is easier for us just to concentrate on where the science is, and indeed we can remember where it was in the 1990s or 2000s.
Koonin was MUCH more concerned about alternative energy and climate than the typical scientist and that was part of his motivation for supporting the Berkeley Energy Biosciences Institute, created 2007. The fact that it was a $500M partnership between Berkeley and BP was a big deal and much debated at the time, but there was never any evidence that the science they did was negatively impacted.
It is IRONIC that his focus on scientific rigor now gets him labeled as a climate denier (or sympathetic to the "wrong" side). ALL scientists should be sceptical, especially about claims regarding long term prediction in complex systems.
Contrast the uncertainty estimates in the IPCC reports (which are not defensible and did not change for ~20y!) vs the (g-2) anomaly that was in the news recently.
When I was at Harvard the physics department and applied science and engineering school shared a coffee lounge. I used to sit there and work in the afternoon and it happened that one of the climate modeling labs had their group meetings there. So for literally years I overheard their discussions about uncertainties concerning water vapor, clouds, etc. which to this day are not fully under control. This is illustrated in Fig1 at the link:https://infoproc.blogspot.c...
The gap between what real scientists say in private and what the public (or non-specialists) gets second hand through the media or politically-focused "scientific policy reports" is vast...
If you don't think we can have long-lasting public delusions regarding "settled science" (like a decade long stock or real estate bubble), look up nuclear winter, which has a lot of similarities to greenhouse gas-driven climate change. Note, I am not claiming that I know with high confidence that nuclear winter can't happen, but I AM claiming that the confidence level expressed by the climate scientists working on it at the time was absurd and communicated in a grotesquely distorted fashion to political leaders and the general public. Even now I would say the scientific issue is not settled, due to its sheer complexity, which is LESS than the complexity involved in predicting long term climate change!
If you have thought a lot about AI and deep learning you may find much of this familiar. Nevertheless I enjoyed the discussion. Apparently Chollet's views (below) are controversial in some AI/ML communities but I do not understand why.
Chollet's Abstraction and Reasoning Corpus (ARC) = Raven's Matrices for AIs :-)
...Francois has a clarity of thought that I've never seen in any other human being! He has extremely interesting views on intelligence as generalisation, abstraction and an information conversation ratio. He wrote on the measure of intelligence at the end of 2019 and it had a huge impact on my thinking. He thinks that NNs can only model continuous problems, which have a smooth learnable manifold and that many "type 2" problems which involve reasoning and/or planning are not suitable for NNs. He thinks that many problems have type 1 and type 2 enmeshed together. He thinks that the future of AI must include program synthesis to allow us to generalise broadly from a few examples, but the search could be guided by neural networks because the search space is interpolative to some extent.
Tim Intro [00:00:00]
Manifold hypothesis and interpolation [00:06:15]
Yann LeCun skit [00:07:58]
Discrete vs continuous [00:11:12]
NNs are not turing machines [00:14:18]
Main show kick-off [00:16:19]
DNN models are locally sensitive hash tables and only efficiently encode some kinds of data well [00:18:17]
Why do natural data have manifolds? [00:22:11]
Finite NNs are not "turing complete" [00:25:44]
The dichotomy of continuous vs discrete problems, and abusing DL to perform the former [00:27:07]
Reality really annoys a lot of people, and ...GPT-3 [00:35:55]
There are type one problems and type 2 problems, but...they are enmeshed [00:39:14]
Chollet's definition of intelligence and how to construct analogy [00:41:45]
How are we going to combine type 1 and type 2 programs? [00:47:28]
Will topological analogies be robust and escape the curse of brittleness? [00:52:04]
Is type 1 and 2 two different physical systems? Is there a continuum? [00:54:26]
Building blocks and the ARC Challenge [00:59:05]
Solve ARC == intelligent? [01:01:31]
Measure of intelligence formalism -- it's a whitebox method [01:03:50]
Generalization difficulty [01:10:04]
Lets create a marketplace of generated intelligent ARC agents! [01:11:54]
Mapping ARC to psychometrics [01:16:01]
New backends for Keras? JAX? [01:20:38]
Intelligence Explosion [01:25:07]
Bottlenecks in large organizations [01:34:29]
Summing up the intelligence explosion [01:36:11]
Post-show debrief [01:40:45]
This is Chollet's paper which is the focus of much of the discussion.
To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems, as well as comparisons with humans. Over the past hundred years, there has been an abundance of attempts to define and measure intelligence, across both the fields of psychology and AI. We summarize and critically assess these definitions and evaluation approaches, while making apparent the two historical conceptions of intelligence that have implicitly guided them. We note that in practice, the contemporary AI community still gravitates towards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks such as board games and video games. We argue that solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience: unlimited priors or unlimited training data allow experimenters to "buy" arbitrary levels of skills for a system, in a way that masks the system's own generalization power. We then articulate a new formal definition of intelligence based on Algorithmic Information Theory, describing intelligence as skill-acquisition efficiency and highlighting the concepts of scope, generalization difficulty, priors, and experience. Using this definition, we propose a set of guidelines for what a general AI benchmark should look like. Finally, we present a benchmark closely following these guidelines, the Abstraction and Reasoning Corpus (ARC), built upon an explicit set of priors designed to be as close as possible to innate human priors. We argue that ARC can be used to measure a human-like form of general fluid intelligence and that it enables fair general intelligence comparisons between AI systems and humans.
Notes on the paper by Robert Lange (TU-Berlin), including illustrations like the ones below.
Last week MSU hosted a virtual meeting on Freedom of Speech and Intellectual Diversity on Campus. I particularly enjoyed several of the talks, including the ones by Randall Kennedy (Harvard), Conor Friesdorf (The Atlantic), and Cory Clark (UPenn). Clark had some interesting survey data I had never seen before. I hope the video from the meeting will be available soon.
In the meantime, here are some survey results from Eric Kaufmann (University of London). The full report is available at the link.
In this recent podcast interview Kaufmann discusses the woke takeover of academia and other institutions.
1. Academia has always been predominantly left, but has become more and more so over time. This imbalance is stronger in Social Science and Humanities (SSH) than in STEM, but even in STEM the faculty are predominantly left of center relative to the general population.
2. Leftists are becoming more and more intolerant of opposing views.
3. Young academics (PhD students and junior faculty) are the least tolerant of all.
In my opinion the unique importance of research universiites originates from their commitment to the search for Truth. This commitment is being supplanted by a focus on social justice, with extremely negative consequences.
Figure 1. Note: Excludes STEM academics. Labels refer to hypothetical scenarios in which respondents are asked whether they would support a campaign to dismiss a staff member who found the respective conclusions in their research. Brackets denote sample size.
Figure 2. Note: Includes STEM academics. Based on a direct question rather than a concealed list technique.
Figure 3. Note: SSH refers to social sciences and humanities. Sample size in brackets. STEM share of survey responses: US and Canada academic: 10%; UK mailout: zero; UK YouGov SSH active: zero; UK YouGov All: 53%; UK PhDs: 55%; North American PhDs: 63%.
This historic panel discussion begins with an introduction to the history of in vitro fertilization (IVF), and describes the latest technologies in genetic screening of embryos. Topics include: polygenic risk scores, bioethics of assisted reproduction, societal acceptance of new reproductive technologies.
At 23:50 Dr. Rafal Smigrodzki tells the story of his daughter Aurea, the first baby born from a polygenically screened embryo (2020).
The panelists include:
Prof. Simon Fishel, the last surviving member of the team that successfully pioneered conception through IVF, leading to the birth of Louise Brown on 25 July 1978. Fishel has been the President of CARE Fertility, the largest IVF provider in the UK.
Elizabeth Carr, the first US IVF baby, who will celebrate her 40th birthday later this year.
Dr. Rafal Smigrodzki (MD PhD), father of Aurea, the first baby born from a polygenically screened embryo.
Prof. Julian Savalescu, Uehiro Chair in Practical Ethics at the University of Oxford. Savulescu coined the phrase procreative beneficence, and is one of the leading philosophers working in bioethics.
Dr. Nathan Treff, Chief Scientist, Genomic Prediction
Jennifer Eccles, Head of Genetic Counseling, Genomic Prediction
Laurent Tellier, CEO, Genomic Prediction
This is a short introduction to polygenic risk screening in IVF:
Randall Kennedy, "The Race Question and Freedom of Expression."
Randall Kennedy is the Michael R. Klein Professor at Harvard Law School, preeminent authority on the First Amendment in its relation to the American struggle for civil rights.
Day One: Intellectual Diversity - Friday, April 9
11:30am - 1:00pm EST
Panel 1: What are the empirical facts about lack of intellectual diversity in academia and what are the causes of existing imbalances?
Paper: Lee Jussim, Distinguished Professor and Chair, Department of Psychology, Rutgers University, author of The Politics of Social Psychology.
Discussant: Philip Tetlock, Annenberg University Professor, University of Pennsylvania, author of “Why so few conservatives and should we care?” and Cory Clark, Visiting Scholar, Department of Psychology, University of Pennsylvania, author of “Partisan Bias and its Discontents.”
2:00pm - 3:30pm EST
Panel 2: In what precise ways and to what degree is this imbalance a problem?
Paper: Joshua Dunn, Professor and Chair, Department of Political Science, University of Colorado, co-author of Passing on the Right: Conservative Professors in the Progressive University.
Discussant: Amna Khalid, Associate Professor of History, Carleton College, author of “Not A Vast Right-Wing Conspiracy: Why Left-Leaning Faculty Should Care About Threats to Free Expression on Campus."
4:00pm - 5:45pm EST
Panel 3: What is To Be Done?
Paper: Musa Al-Gharbi, Paul F. Lazarsfeld Fellow in Sociology, Columbia University and Managing Editor, Heterodox Academy, author of “Why Care About Ideological Diversity in Social Research? The Definitive Response.”
Paper: Conor Friedersdorf, Staff writer at The Atlantic and frequent contributor to its special series “The Speech Wars,” author of “Free Speech Will Survive This Moment.”
Day Two: Freedom of Speech - Saturday, April 10
11:30am - 1:00pm EST
Panel 1: An empirical accounting of the recent challenges to free speech on campus from left and right. What is the true character of the problem or problems here and do they constitute a “crisis”?
Paper: Jonathan Marks, Professor and Chair, Department of Politics and International Relations, Ursinus College, author of Let's Be Reasonable: A Conservative Case for Liberal Education.
Respondent: April Kelly-Woessner, Dean of the School of Public Service and Professor of Political Science at Elizabethtown College, author of The Still Divided Academy
2:00pm - 3:45pm EST
Panel 2: But is Free speech, as traditionally interpreted, even the right ideal? -- a Debate
Ulrich Baer, University Professor of Comparative Literature, German, and English, NYU, author of What Snowflakes Get Right: Free Speech and Truth on Campus
Keith Whittington, Professor of Politics, Princeton University, author of Speak Freely: Why Universities Must Defend Free Speech.
4:30pm - 6:15pm EST
Panel 3: What is To Be Done?
Paper: Nancy Costello, Associate Clinical Professor of Law, MSU. Founder and Director of the First Amendment Law Clinic -- the only law clinic in the nation devoted to the defense of student press rights. Also, Director of the Free Expression Online Library and Resource Center.
Paper: Jonathan Friedman, Project Director for campus free speech at PEN America – “a program of advocacy, analysis, and outreach in the national debate around free speech and inclusion at colleges and universities.”
These new results arose from initial investigations of blood biomarker predictions from DNA. The lipoprotein A predictor we built correlates almost 0.8 with the measured result, and this agreement would probably be even stronger if day to day fluctuations were averaged out. It is the most accurate genomic predictor for a complex trait that we are aware of.
We then became interested in the degree to which biomarkers alone could be used to predict disease risk.
Some of the biomarker-based disease risk predictors we built (e.g., for kidney or liver problems) do not, as far as we know, have widely used clinical counterparts. Further research may show that predictors of this kind have broad utility.
Statistical learning in a space of ~50 biomarkers is considered a "high dimensional" problem from the perspective of medical diagnosis, however compared to genomic prediction using a million SNP features, it is rather straightforward.
Erik Widen, Timothy G. Raben, Louis Lello, Stephen D.H. Hsu
We use UK Biobank data to train predictors for 48 blood and urine markers such as HDL, LDL, lipoprotein A, glycated haemoglobin, ... from SNP genotype. For example, our predictor correlates ~ 0.76 with lipoprotein A level, which is highly heritable and an independent risk factor for heart disease. This may be the most accurate genomic prediction of a quantitative trait that has yet been produced (specifically, for European ancestry groups). We also train predictors of common disease risk using blood and urine biomarkers alone (no DNA information). Individuals who are at high risk (e.g., odds ratio of > 5x population average) can be identified for conditions such as coronary artery disease (AUC ~ 0.75), diabetes (AUC ~ 0.95), hypertension, liver and kidney problems, and cancer using biomarkers alone. Our atherosclerotic cardiovascular disease (ASCVD) predictor uses ~10 biomarkers and performs in UKB evaluation as well as or better than the American College of Cardiology ASCVD Risk Estimator, which uses quite different inputs (age, diagnostic history, BMI, smoking status, statin usage, etc.). We compare polygenic risk scores (risk conditional on genotype: (risk score | SNPs)) for common diseases to the risk predictors which result from the concatenation of learned functions (risk score | biomarkers) and (biomarker | SNPs).
The first three videos below are episodes of Japanese director Takeuchi Ryo's ongoing series on Huawei.
Ryo lives in Nanjing and speaks fluent Mandarin. He became famous for his coverage of the lockdown and pandemic in Wuhan. The fourth video below tells the stories of 10 families: how they survived, and how their lives have changed.
The general consensus seems to be that Huawei is 2+ years ahead of other competitors in 5G technology, and has a very deep IP position (patent portfolio) as well. In AI applications my impression is that they are also strong, but not world leaders at the research frontier like Google Brain or DeepMind. Like most Chinese companies their strength is in practical deployment of systems at scale, not in publishing papers. In smartphones and laptops they compete head to head with Samsung, Apple, etc. in all areas, including chip design. Their HiSilicon subsidiary has designed Kirin CPUs that are on par with the best Qualcomm and Apple competitors used in flagship handsets. However, all three rely on TSMC to fabricate these designs.