The BLM Movement Will Fade Into Black

We, as veterans, have a different approach to freedom and the importance of the American Flag.  The American Flag, like the Confederate Flag during the Civil War, was a symbol of the Confederate States and what they believed in...  and, to fly that Confederate Flag when there are no more Confederate States, is just as misleading as it is for Americans to fly a BLM flag instead of an American Flag, unless those who want to fly the BLM flag have a desire, like to Confederacy, to create their own country...  which I doubt the rest of the United States would allow them to do, especially since the rest of the United States is bending over backwards to make sure that those who are supporting BLM, are sufficiently PATRONIZED and PACIFIED.

And just like all other movements that have taken place within the continental borders of the contiguous States, BLM will fade and dissipate into the atmosphere of our previous existence.  That is to say, whatever happens right now, this moment, will only last this moment until this moment passes and it is in our past.  For instance, the wrongful and illegal death of George Floyd is now and forever more relegated to our past...  and, will remain in our past, no matter how often we try to remember it.

The events of 9/11 are now being treated the same way...  they are in our past and like everything else in our past, it is gone...  passed away...  only memories slowly fading remain.

• Vietnam is in our past
• The assassinations of JFK and MLK, Jr are in our past
• The Stock Market crash of 2008 is in our past
• Richard Nixon's resignation is in our past
• The killing of Osama bin Laden is in our past
• COVID 19 will soon be in our past

Just like all other movements, the BLM movement will continually as long as it is politically convenient for it to continue and when that stops, the movement will fade...  and, in 20 years, we will again look back into our past and retrieve the memory and the reminder that nothing really changed.

12% of the population cannot change 60% of the population for very long...  there are simply not enough votes and not enough political capital for anything like BLM to be sustainable.

REMEMBER, all the tax payer dollars that we pump into CLIMATE CHANGE will do absolutely no good at all as long as CHINA, INDIA, AFRICA, SOUTH AMERICA, the MIDDLE EAST, ASIA, and RUSSIA join in with us...  and, the last time I looked, none of those countries were planning to participate...

CLIMATE CONTROL and BLM will perish on the political vine of apathy and our inability to get the rest of the world to follow us...

WHO IS REALLY GOING TO FOLLOW BLM?  Especially after we defund the police (because of George Floyd) and realize what a frigging stupid MISTAKE that was...  especially for the blacks and other minorities because no matter how much we raise the minimum wage and force employers to pay more, these poor minority souls will not be able to relocate outside of where they are...

AND... how is the BLM movement going to stop the progress and technology and the marriage of artificial intelligence with robots?  Robots WILL REPLACE black and while workers alike and robots will be ubiquitous by the year 2030...  which is less than 9 years away...  only 2.5 Presidential terms away...

Can BLM matter hold on for 9 more years???

Machine Learning Enhanced

FROM ZDNET...

IBM is releasing a new module as part of its open-source quantum software development kit, Qiskit, to let developers leverage the capabilities of quantum computers to improve the quality of their machine-learning models.

Qiskit Machine Learning is now available and includes the computational building blocks that are necessary to bring machine-learning models into the quantum space.

Machine learning is a branch of artificial intelligence that is now widely used in almost every industry. The technology is capable of crunching through ever-larger datasets to identify patterns and relationships, and eventually discover the best way to calculate an answer to a given problem.

Researchers and developers, therefore, want to make sure that the software comes up with the most optimal model possible – which means expanding the amount and improving the quality of the training data that is fed to the machine-learning software. This process inevitably comes with higher costs and much longer training times.

Delegating some parts of the process to a quantum computer could resolve these issues, by speeding up the time it takes to train or evaluate a machine-learning model, but also by vastly increasing what is known as the feature space – the collection of features that are used to characterize the data that is fed to the model, for example "gender" or "age" if the system is being trained to recognize patterns about people.  READ MORE

 https://www.zdnet.com/article/ibms-new-tool-lets-developers-add-quantum-computing-power-to-machine-learning/

Algorithms

FROM WIRED...

IN 2012, ARTIFICIAL intelligence researchers engineered a big leap in computer vision thanks, in part, to an unusually large set of images—thousands of everyday objects, people, and scenes in photos that were scraped from the web and labeled by hand. That data set, known as ImageNet, is still used in thousands of AI research projects and experiments today.

But last week every human face included in ImageNet suddenly disappeared—after the researchers who manage the data set decided to blur them.

Just as ImageNet helped usher in a new age of AI, efforts to fix it reflect challenges that affect countless AI programs, data sets, and products.


“We were concerned about the issue of privacy,” says Olga Russakovsky, an assistant professor at Princeton University and one of those responsible for managing ImageNet.

ImageNet was created as part of a challenge that invited computer scientists to develop algorithms capable of identifying objects in images. In 2012, this was a very difficult task. Then a technique called deep learning, which involves “teaching” a neural network by feeding it labeled examples, proved more adept at the task than previous approaches.


Since then, deep learning has driven a renaissance in AI that also exposed the field’s shortcomings. For instance, facial recognition has proven a particularly popular and lucrative use of deep learning, but it's also controversial. A number of US cities have banned government use of the technology over concerns about invading citizens’ privacy or bias, because the programs are less accurate on nonwhite faces.

Today ImageNet contains 1.5 million images with around 1,000 labels. It is largely used to gauge the performance of machine learning algorithms, or to train algorithms that perform specialized computer vision tasks. Blurring the faces affected 243,198 of the images.  READ MORE

Machine Learning

As reported by Leah Crane:

Machine learning, a process used to train artificial intelligences, can take an extremely long time – but a quantum trick could massively speed things up for tasks involving particles of light called photons.

In reinforcement learning, an algorithm runs through the same problem over and over again and is given a numerical reward only when it reaches the correct answer. That process teaches it to find the correct answer more quickly when pitted against similar problems later on.

Now Valeria Saggio at the University of Vienna in Austria and her colleagues have added a quantum twist to accelerate this process. They set up an experiment involving a photon moving through a wave guide and ending up in one of four possible states. They tasked an AI with making sure the photon ended up in one particular state, and rewarded it for doing so.

In the classical version of this experiment, without any added quantum effects, the AI would only be able to move the photon to one specific state at a time, being rewarded when it made a correct guess. However, in the quantum version of the experiment, the AI could put the photon in a superposition of more than one state. This allowed it to narrow down the correct answer before making a final, classical guess at the goal state.

“Imagine you have a robot that is standing at a crossroads, and the robot has two options – it can go left or it can go right,” says Saggio. “If the robot goes right, it does not receive a reward, but if it goes left it receives a reward. At the next round, the probability of it going left will increase.”

That’s the classical version of the experiment, but the quantum version would allow it to go left and right simultaneously at each guess, requiring far fewer guesses before it learns to always go left. This strategy sped up the learning time of the AI by 63 per cent, from 270 guesses to just 100.

Artificial Intelligence

“Everything we love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before – as long as we manage to keep the technology beneficial.“  Max Tegmark, President of the Future of Life Institute

WHAT IS AI?
From SIRI to self-driving cars, artificial intelligence (AI) is progressing rapidly. While science fiction often portrays AI as robots with human-like characteristics, AI can encompass anything from Google’s search algorithms to IBM’s Watson to autonomous weapons.

Artificial intelligence today is properly known as narrow AI (or weak AI), in that it is designed to perform a narrow task (e.g. only facial recognition or only internet searches or only driving a car). However, the long-term goal of many researchers is to create general AI (AGI or strong AI). While narrow AI may outperform humans at whatever its specific task is, like playing chess or solving equations, AGI would outperform humans at nearly every cognitive task.

HOW CAN AI BE DANGEROUS?
Most researchers agree that a superintelligent AI is unlikely to exhibit human emotions like love or hate, and that there is no reason to expect AI to become intentionally benevolent or malevolent. Instead, when considering how AI might become a risk, experts think two scenarios most likely:

• The AI is programmed to do something devastating: Autonomous weapons are artificial intelligence systems that are programmed to kill. In the hands of the wrong person, these weapons could easily cause mass casualties. Moreover, an AI arms race could inadvertently lead to an AI war that also results in mass casualties. To avoid being thwarted by the enemy, these weapons would be designed to be extremely difficult to simply “turn off,” so humans could plausibly lose control of such a situation. This risk is one that’s present even with narrow AI, but grows as levels of AI intelligence and autonomy increase.
• The AI is programmed to do something beneficial, but it develops a destructive method for achieving its goal: This can happen whenever we fail to fully align the AI’s goals with ours, which is strikingly difficult. If you ask an obedient intelligent car to take you to the airport as fast as possible, it might get you there chased by helicopters and covered in vomit, doing not what you wanted but literally what you asked for. If a superintelligent system is tasked with a ambitious geoengineering project, it might wreak havoc with our ecosystem as a side effect, and view human attempts to stop it as a threat to be met.

As these examples illustrate, the concern about advanced AI isn’t malevolence but competence. A super-intelligent AI will be extremely good at accomplishing its goals, and if those goals aren’t aligned with ours, we have a problem. You’re probably not an evil ant-hater who steps on ants out of malice, but if you’re in charge of a hydroelectric green energy project and there’s an anthill in the region to be flooded, too bad for the ants. A key goal of AI safety research is to never place humanity in the position of those ants.  TO READ ENTIRE ARTICLE, Click Here...