Wednesday, April 17, 2019
Tuesday, April 16, 2019
The scenes of the Fire at Notre-Dame yesterday were heartbreaking to be witness to. We commend the valiant fire fighters in Paris on all that they were able to save. We were so pleased to see the commitment by Salma Hayek and her husband for $ 113 Million and the commitment by President Macron to rebuild the Notre-Dame. This is as this crossed our news wire on an upcoming international donors conference--We will assist in helping to spread the World on reconstruction in every which way as Notre-Dame is not just part of France, but the World--as we also hope France sees a period of calm and unity as we urge the yellow jackets to stand down:
Monday, April 15, 2019
As a new week begins, we wanted to report on this perspective on the week that was with what Seth Meyers noted. This is as The US Tresuary Department refused the US House of Representatives request for President Trump's Tax Return. DC Reports provided some alternative perspective on it:
Trump and his team are now openly declaring that Trump is above the law, a clear step toward a dictatorship. Our David Cay Johnston explains in the first of several pieces examining official lawlessness.
Also, worker deaths from falls are rising at an alarming rate, reporting by Eli Wolfe of FairWarning reveals.
Please read our latest and pass along to others what you learn.
Counselor to the President joined Meet the Press to provide the Administration's View:
The Meet the Press shared its' perspective:
This is also Earth Day Week. As our team will be "kind of dark", we will continue to assess the on going developments as we assess the Immigration Debate, the Mueller Report and the controversy over Attorney General Barr, the continued challenges behind Brexit, the challenges in the Middle East, Elections in India, Australia and Ukraine and East Africa--including the situation in Sudan.
It will be an interesting week as our team will have twice a week updates in our Google Corner this week as we look forward to community engagement.
Saturday, April 13, 2019
We have been reporting on the Iran Floods as we have been witness to unprecedented floods. We decided to feature this courtesy of Peter Diamandis about the possibilities to be able to be proactive in dealing with disasters:
Between 2005 and 2014, natural disasters have claimed the lives of over 700,000 people and resulted in total damage of more than US$1.4 trillion.
During the past 50 years, the frequency of recorded natural disasters has surged nearly five-fold.
In this blog, I’ll be exploring how converging exponential technologies (AI, Robotics, Drones, Sensors, Networks) are transforming the future of disaster relief — how we can prevent them in the first place and get help to victims during that first golden hour wherein immediate relief can save lives.
Here are the three areas of greatest impact:
Let’s dive in!
Artificial Intelligence and Predictive Mapping
When it comes to immediate and high-precision emergency response, data is gold.
Already, the meteoric rise of space-based networks, stratosphere-hovering balloons, and 5G telecommunications infrastructure is in the process of connecting every last individual on the planet.
Aside from democratizing the world’s information, however, this upsurge in connectivity will soon grant anyone the ability to broadcast detailed geotagged data, particularly those most vulnerable to natural disasters.
Armed with the power of data broadcasting and the force of the crowd, disaster victims now play a vital role in emergency response, turning a historically one-way blind rescue operation into a two-way dialogue between connected crowds and smart response systems.
With a skyrocketing abundance of data, however, comes a new paradigm: one in which we no longer face a scarcity of answers. Instead, it will be the quality of our questions that matters most.
This is where AI comes in: our mining mechanism.
In the case of emergency response, what if we could strategically map an almost endless amount of incoming data points? Or predict the dynamics of a flood and identify a tsunami’s most vulnerable targets before it even strikes? Or even amplify critical signals to trigger automatic aid by surveillance drones and immediately alert crowdsourced volunteers?
Already, a number of key players are leveraging AI, crowdsourced intelligence, and cutting edge visualizations to optimize crisis response and multiply relief speeds.
Take One Concern, for instance.
Born out of Stanford under the mentorship of leading AI expert Andrew Ng, One Concern leverages AI through analytical disaster assessment and calculated damage estimates.
Partnering with the City of Los Angeles, San Francisco, and numerous cities in San Mateo County, the platform assigns verified, unique ‘digital fingerprints’ to every element in a city. Building robust models of each system, One Concern’s AI platform can then monitor site-specific impacts of not only climate change but each individual natural disaster, from sweeping thermal shifts to seismic movement.
This data, combined with that of city infrastructure and former disasters are then used to predict future damage under a range of disaster scenarios, informing prevention methods and structures in need of reinforcement.
Within just four years, One Concern can now make precise predictions with an 85 percent accuracy rate under 15 minutes.
And as IoT-connected devices and intelligent hardware continue to boom, a blooming trillion-sensor economy will only serve to amplify AI’s predictive capacity, offering us immediate, preventive strategies long before disaster strikes.
Beyond natural disasters, however, crowdsourced intelligence, predictive crisis mapping, and AI-powered responses are just as formidable a triage in humanitarian disasters.
One extraordinary story is that of Ushahidi.
When violence broke out after the 2007 Kenyan elections, one local blogger proposed a simple yet powerful question to the web: “Any techies out there willing to do a mashup of where the violence and destruction is occurring and put it on a map?”
Within days, four ‘techies’ heeded the call, building a platform that crowdsourced first-hand reports via SMS, mined the web for answers, and — with over 40,000 verified reports — sent alerts back to locals on the ground and viewers across the world.
Today, Ushahidi has been used in over 150 countries, reaching a total of 20 million people across 100,000+ deployments. Now an open-source crisis-mapping software, its V3 (or “Ushahidi in the Cloud”) is accessible to anyone, mining millions of Tweets, hundreds of thousands of news articles, and geotagged, time-stamped data from countless sources.
Aggregating one of the longest-running crisis maps to date, Ushahidi’s Syria Tracker has proved invaluable in the crowdsourcing of witness reports. Providing real-time geographic visualizations of all verified data, Syria Tracker has enabled civilians to report everything from missing people and relief supply needs, to civilian casualties and disease outbreaks — all while evading the government’s cell network, keeping identities private, and verifying reports prior to publication.
As mobile connectivity and abundant sensors converge with AI-mined crowd intelligence, real-time awareness will only multiply in speed and scale.
Imagining the Future….
Within the next 10 years, spatial web technology might even allow us to tap into mesh networks.
As I’ve explored in a previous blog on the implications of the spatial web, while traditional networks rely on a limited set of wired access points (or wireless hotspots), a wireless mesh network can connect entire cities via hundreds of dispersed nodes that communicate with each other and share a network connection non-hierarchically.
In short, this means that individual mobile users can together establish a local mesh network using nothing but the compute power in their own devices.
Take this a step further, and a local population of strangers could collectively broadcast countless 360-degree feeds across a local mesh network.
Imagine a scenario in which armed attacks break out across disjointed urban districts, each cluster of eye witnesses and at-risk civilians broadcasting an aggregate of 360-degree videos, all fed through photogrammetry AIs that build out a live hologram in real time, giving family members and first responders complete information.
Or take a coastal community in the throes of torrential rainfall and failing infrastructure. Now empowered by a collective live feed, verification of data reports takes a matter of seconds, and richly layered data informs first responders and AI platforms with unbelievable accuracy and specificity of relief needs.
By linking all the right technological pieces, we might even see the rise of automated drone deliveries. Imagine: crowdsourced intelligence is first cross-referenced with sensor data and verified algorithmically. AI is then leveraged to determine the specific needs and degree of urgency at ultra-precise coordinates. Within minutes, once approved by personnel, swarm robots rush to collect the requisite supplies, equipping size-appropriate drones with the right aid for rapid-fire delivery.
This brings us to a second critical convergence: robots and drones.
While cutting-edge drone technology revolutionizes the way we deliver aid, new breakthroughs in AI-geared robotics are paving the way for superhuman emergency responses in some of today’s most dangerous environments.
Let’s explore a few of the most disruptive examples to reach the testing phase.
Autonomous Robots and Swarm Solutions
As hardware advancements converge with exploding AI capabilities, disaster relief robots are graduating from assistance roles to fully autonomous responders at a breakneck pace.
Born out of MIT’s Biomimetic Robotics Lab, the Cheetah III is but one of many robots that may form our first line of defense in everything from earthquake search-and-rescue missions to high-risk ops in dangerous radiation zones.
Now capable of running at 6.4 meters per second, Cheetah III can even leap up to a height of 60 centimeters, autonomously determining how to avoid obstacles and jump over hurdles as they arise.
Source: Massachusetts Institute of Technology (MIT)
Initially designed to perform spectral inspection tasks in hazardous settings (think: nuclear plants or chemical factories), the Cheetah’s various iterations have focused on increasing its payload capacity, range of motion, and even a gripping function with enhanced dexterity.
But as explained by the Lab’s director and MIT Associate Professor Sangbae Kim, Cheetah III and future versions are aimed at saving lives in almost any environment: “Let’s say there’s a fire or high radiation, [whereby] nobody can even get in. [It’s in these circumstances that] we’re going to send a robot [to] check if people are inside. [...] [And even] before doing all that, the short-term goal will be sending [the] robot where we don’t want to send humans at all, [...] for example, toxic areas or [those with] mild radiation.”
And the Cheetah III is not alone.
Just this February, Tokyo’s Electric Power Company (TEPCO) has put one of its own robots to the test.
For the first time since Japan’s devastating 2011 tsunami, which led to three nuclear meltdowns in the nation’s Fukushima nuclear power plant, a robot has successfully examined the reactor’s fuel.
Broadcasting the process with its built-in camera, the robot was able to retrieve small chunks of radioactive fuel at five of the six test sites, offering tremendous promise for long-term plans to clean up the still-deadly interior.
Also out of Japan, Mitsubishi Heavy Industries (MHi) is even using robots to fight fires with full autonomy. In a remarkable new feat, MHi’s Water Cannon Bot can now put out blazes in difficult-to-access or highly dangerous fire sites.
Delivering foam or water at 4,000 liters per minute and 1 megapascal (MPa) of pressure, the Cannon Bot and its accompanying Hose Extension Bot even form part of a greater AI-geared system to conduct reconnaissance and surveillance on larger transport vehicles.
As wildfires grow ever more untamable, high-volume production of such bots could prove a true lifesaver. Paired with predictive AI forest fire mapping and autonomous hauling vehicles, not only will solutions like MHi’s Cannon Bot save numerous lives, but avoid population displacement and paralyzing damage to our natural environment before disaster has the chance to spread.
But even in cases where emergency shelter is needed, groundbreaking (literally) robotics solutions are fast to the rescue.
After multiple iterations by Fastbrick Robotics, the Hadrian X end-to-end bricklaying robot can now autonomously build a fully livable, 180-square meter home in under 3 days. Using a laser-guided robotic attachment, the all-in-one brick-loaded truck simply drives to a construction site and directs blocks through its robotic arm in accordance with a 3D model.
Source: Fastbrick Robotics
Meeting verified building standards, Hadrian and similar solutions hold massive promise in the long-term, deployable across post-conflict refugee sites and regions recovering from natural catastrophes.
But what if we need to build emergency shelters from local soil at hand? Marking an extraordinary convergence between robotics and 3D printing, the Institute of Advanced Architecture of Catalonia (IAAC) is already working on a solution.
In a major feat for low-cost construction in remote zones, IAAC has found a way to convert almost any soil into a building material with three times the tensile strength of industrial clay. Offering myriad benefits, including natural insulation, low GHG emissions, fire protection, air circulation and thermal mediation, IAAC’s new 3D printed native soil can build houses on-site for as little as $1,000.
But while cutting edge robotics unlock extraordinary new frontiers for low-cost, large-scale emergency construction, novel hardware and computing breakthroughs are also enabling robotic scale at the other extreme of the spectrum.
Again, inspired by biological phenomena, robotics specialists across the U.S. have begun to pilot tiny robotic prototypes for locating trapped individuals and assessing infrastructural damage.
Take RoboBees, tiny Harvard-developed bots that use electrostatic adhesion to ‘perch’ on walls and even ceilings, evaluating structural damage in the aftermath of an earthquake.
Or Carnegie Mellon’s prototyped Snakebot, capable of navigating through entry points that would otherwise be completely inaccessible to human responders. Driven by AI, the Snakebot can maneuver through even the most densely packed rubble to locate survivors, using cameras and microphones for communication.
But when it comes to fast-paced reconnaissance in inaccessible regions, miniature robot swarms have good company.
Next-Generation Drones for Instantaneous Relief Supplies
Particularly in the case of wildfires and conflict zones, autonomous drone technology is fundamentally revolutionizing the way we identify survivors in need and automate relief supply.
Not only are drones enabling high-resolution imagery for real-time mapping and damage assessment, but preliminary research shows that UAVs far outpace ground-based rescue teams in locating isolated survivors.
As presented by a team of electrical engineers from the University of Science and Technology of China, drones could even build out a mobile wireless broadband network in record time using a “drone-assisted multi-hop device-to-device” program.
And as shown during Houston’s Hurricane Harvey, drones can provide scores of predictive intel on everything from future flooding to damage estimates.
Among multiple others, a team led by Texas A&M computer science professor and director of the university’s Center for Robot-Assisted Search and Rescue Dr. Robin Murphy flew a total of 119 drone missions over the city, from small-scale quadcopters to military-grade unmanned planes. Not only were these critical for monitoring levee infrastructure, but also for identifying those left behind by human rescue teams.
But beyond surveillance, UAVs have begun to provide lifesaving supplies across some of the most remote regions of the globe.
One of the most inspiring examples to date is Zipline.
Created in 2014, Zipline has completed 12,352 life-saving drone deliveries to date. While drones are designed, tested and assembled in California, Zipline primarily operates in Rwanda and Tanzania, hiring local operators and providing over 11 million people with instant access to medical supplies.
Providing everything from vaccines and HIV medications to blood and IV tubes, Zipline’s drones far outpace ground-based supply transport, in many instances providing life-critical blood cells, plasma and platelets in under an hour.
But drone technology is even beginning to transcend the limited scale of medical supplies and food.
Now developing its drones under contracts with DARPA and the U.S. Marine Corps, Logistic Gliders, Inc. has built autonomously navigating drones capable of carrying 1,800 pounds of cargo over unprecedented long distances.
Built from plywood, Logistic’s gliders are projected to cost as little as a few hundred dollars each, making them perfect candidates for high-volume, remote aid deliveries, whether navigated by a pilot or self-flown in accordance with real-time disaster zone mapping.
As hardware continues to advance, autonomous drone technology coupled with real-time mapping algorithms pose no end of abundant opportunities for aid supply, disaster monitoring, and richly layered intel previously unimaginable for humanitarian relief.
Perhaps one of the most consequential and impactful applications of converging technologies is their transformation of disaster relief methods.
While AI-driven intel platforms crowdsource firsthand experiential data from those on the ground, mobile connectivity and drone-supplied networks are granting newfound narrative power to those most in need.
And as a wave of new hardware advancements gives rise to robotic responders, swarm technology and aerial drones, we are fast approaching an age of instantaneous and efficiently distributed responses, in the midst of conflict and natural catastrophes alike.
Empowered by these new tools, what might we create when everyone on the planet has the same access to relief supplies and immediate resources? In a new age of prevention and fast recovery, what futures can you envision?
Friday, April 12, 2019
Thursday, April 11, 2019
While out & about in our World...
Wednesday, April 10, 2019
Sudan has been in the midst of a Revolution--and the Armed Forces came out and noted how they understood the aspirations of the people while underscoring that they will not allow chaos to reign. We could not help but be inspired by how women are at the front--as Joyce Karam of the National so beautifully noted in a Tweet, remember the voice--Power to the people:
Tuesday, April 9, 2019
This is as this clip on the frustruation of the ordinary faces with the Government in power was evident:
Going viral in #Iran social media today: The man took a jab at the Governor of province of Khuzestan by saying “will you do a thing for the #flooded people here or keep [spending money] in #Syria instead?” The Governor loses his cool & says “go away you rude anti regime” pic.twitter.com/STQiTmrbnx
— Bahman Kalbasi (@BahmanKalbasi) April 8, 2019
Monday, April 8, 2019
It has been quite a weekend as the Acting White House Chief of Staff said Democrats will never the President's Tax Returns in spite of provisions in the law that gives the US House of Representatives the statutory authority to do so--and that the Secretary of Homeland Security resigned--in essence fired by the President as the President visited the border and headlined a fundraising event in California.
The following reflects high level Trump Administration Departures over the past two years:
Top Trump Departures— Kyle Griffin (@kylegriffin1) April 7, 2019
This is as the 2020 Campaign is well on its' way as a number of new entrants joined the quest for the Democratic Nomination for President--one of the venues they spoke at was the National Action Network Conference. As the campaign is heating up, we hereby present this courtesy of the team at Crystal Ball as the Presidential Campaign of 2020 begins in earnest:
KEY POINTS FROM THIS ARTICLE
-- The author’s “time for change” presidential forecasting model has a successful track record of projecting presidential elections. In 2016, it showed Donald Trump as a favorite to win the national popular vote. Though Trump ultimately lost the popular vote while winning the Electoral College, the model presented an early indication that Trump was more than capable of winning the 2016 election.
-- A new model uses post-World War II presidential elections featuring incumbents to try to project Trump’s chances in 2020 based on his approval rating and the state of the economy.
-- Based on Trump’s current average approval rating in national polls and modest economic growth, the model suggests an extremely close 2020 result in the Electoral College.
-- The model’s final projection will come out next summer.
Forecasting Trump’s Electoral College showing
There are still 10 months to go until the first ballots will be cast in the 2020 presidential caucuses and primaries. The identity of the Democratic nominee, in all likelihood, will not be known for more than a year. Nevertheless, it is not too early to begin speculating about Donald Trump’s chances of winning a second term in the White House. That is because when an incumbent is running for reelection, the presidential election is largely a referendum on that incumbent’s performance. The challenger’s characteristics and the general election campaign itself matter only at the margins.
In this article, I present the results of a modified version of the “time for change” forecasting model that I have used to correctly predict the winner of the national popular vote in every presidential election between 1992 and 2012. And while the model failed to predict Hillary Clinton’s narrow popular vote victory in 2016, I noted at the time that Trump might well fall short of his predicted vote given his extraordinary weaknesses as a candidate. As it was, the model predicted Trump to win the national two-party popular vote by three points, but Clinton won it by two points.
The time for change model predicts the outcomes of presidential elections based on three factors: the incumbent president’s net approval rating in late June or early July, the change in annualized real GDP in the second quarter of the election year, and a dummy variable based on whether the president’s party has held the White House for only one term or for more than one term. However, the model presented here differs in two crucial respects from the traditional time for change model. First, the model attempts to predict the electoral vote, not the popular vote. The reasons for this change should be obvious: the winner of the election is determined by the electoral vote and in two of the past five elections, 2000 and 2016, the winner of the popular vote lost the electoral vote.
The second difference between this model and the earlier time for change model is that this one is based only on the 11 presidential elections since World War II in which an incumbent was running. That is because an examination of the data on all 18 presidential elections since World War II indicates that elections with a running incumbent are different -- their outcomes are much more predictable based on the incumbent’s approval rating in the middle of the election year and the growth rate of the economy during the second quarter of the year. Thus, the correlations between real GDP growth and incumbent electoral vote is .48 for the seven open seat elections vs. .73 for the 11 incumbent elections. Likewise, the correlation between late June presidential approval and incumbent electoral vote is .56 for the seven open seat elections vs. .82 for the 11 incumbent elections.
Table 1: Estimates for Incumbent Electoral Vote Model
Source: Data compiled by author.
Table 1 presents the estimates for the ordinary least squares regression analysis of incumbent electoral vote on late June presidential approval, second quarter GDP change, and the time for change dummy variable for the 11 incumbent elections since World War II. The results show that the model does an excellent job of predicting the electoral vote for the incumbent, explaining over 90% of the variance in election results. All three predictors have very strong and highly significant effects, despite the small number of elections used in the analysis. The estimated coefficients indicate that an increase of one point in the incumbent’s net approval rating is associated with an increase of almost 2.5 electoral votes. Likewise, every one point in real GDP growth is associated with an increase of almost 20 electoral votes. Finally, the results in Table 1 show that first-term incumbents enjoy a substantial predicted advantage of just over 114 electoral votes over incumbents running for a second or later term in the White House for their party.
Table 2: Predicted and actual electoral vote for incumbents in presidential elections, 1948-2012
Note: President Gerald Ford’s formal electoral vote count in 1976 was 240 because of a rogue elector in Washington state, but for the purposes of this article we are disregarding the rogue elector and listing his total as 241, because that is what he earned from the election results.
Source: Data compiled by author.
Table 2 displays the predictions generated by the model for the 11 incumbent elections since World War II along with the actual results of these elections. The model predicts the correct winner of all 11 elections. The margin of error for these predictions ranges from two electoral votes in 1972 to 73 electoral votes in 1984 with an average error of 26 electoral votes.
Based on this impressive performance, we can examine the range of likely forecasts of the electoral vote in the 2020 presidential election, assuming that Donald Trump is the Republican nominee. These conditional forecasts are presented in Table 3 based on a range of possible scenarios for real GDP growth and presidential approval. Most economic forecasts for 2020 have GDP growth falling in the 1% to 2% range, despite the administration’s official forecast of much higher growth. I have included growth rates ranging from zero to 3%. And since President Trump’s net approval rating has never reached the neutral point in any polling average or any reputable national poll since his first days in office, I have set the range for the approval rating variable from -20 to zero. However, readers can easily plug in their own preferred values by using the weights generated by the regression model in Table 1.
Table 3: Predicted electoral vote for Donald Trump by second quarter GDP change and late June net approval rating
Source: Data compiled by author.
The results in Table 3 show a wide range of potential outcomes, from near certain defeat for the incumbent if the economy stalls out and his approval rating falls far below the neutral point, as it has from time to time, to near certain victory if the economy grows faster than expected and his approval rating rises to the neutral point, where it has essentially never been in his first two-plus years in office.
The most plausible prediction at this point, however, is for a very close contest. Given a net approval rating of -10, approximately where Trump’s approval rating has been stuck for most of the past year, and real GDP growth of between 1% to 2%, in line with most recent economic forecasts, the model predicts that he would receive between 263 and 283 electoral votes. Of course, it takes 270 electoral votes to win.
There are a few caveats that need to be mentioned at this point. First, this model is based on a very limited number of elections. Despite its apparent accuracy, therefore, any predictions should be taken with a grain of salt. Second, it is not certain that President Trump will receive the same first-term boost as his predecessors. This first-term boost is his most significant advantage heading into the 2020 election. However, the fact that his performance fell short of that predicted by the model in 2016 suggests that his highly unconventional style and abrasive personality could result in a similar underperformance in 2020.
Finally, it needs to be mentioned that none of the 11 elections included in this analysis resulted in an Electoral College misfire. In all 11 incumbent elections since World War II, the winner of the popular vote also won the electoral vote. Indeed, the correlation between the two is a very robust .95. These results suggest that a misfire of the magnitude that allowed Trump to win an Electoral College majority while losing the popular vote by more than two percentage points, is not very likely. But it is possible and in such a case, our model’s prediction may miss the mark by a larger than usual margin.
What is critical is this: there is never a dull moment....
Saturday, April 6, 2019
Friday, April 5, 2019
As President Trump's threat to close the Southern Border lingers on and as aid has been cut to Guatemala, El Salvador and Honduras, Governor Newsom will be leaving for El Salvador as noted by the team at CalMatters:
Thursday, April 4, 2019
Yesterday was the Third Anniversary of the detention of Nazanin Zargari Radcliff by the Iranian Government. We have had the honor to add our voice to ask that she is released--as we have also been following the case of other dual Nationals in Iran. We hereby present the update by Mr. Radcliffe as we urge the Iranian Government to release Mrs. Radcliffe immediately and without delay as we hereby note the link for all to sign the petition: