Bitcoin (BTC) Down $78.19 Over Past 4 Hours, Moves Up For the 2nd Day In A Row; Pin Bar Pattern Appearing on Chart – CFDTrading

Bitcoin 4 Hour Price Update

Updated July 31, 2020 07:18 PM GMT (03:18 PM EST)

Bitcoins 3 four-hour candle positive streak has officially concluded, as the candle from the last 4 hour candle closed down 0.69% ($78.19). On a relative basis, Bitcoin was the worst performer out of all 5 of the assets in the Top Cryptos asset class during the last 4 hour candle.

Bitcoin is up 0.01% ($1.62) since the day prior, marking the 2nd day in a row an upward move has occurred. The price move occurred on volume that was down 26.94% from the day prior, but up 21.32% from the same day the week before. On a relative basis, Bitcoin was the worst performer out of all 5 of the assets in the Top Cryptos asset class during the day prior. The daily price chart of Bitcoin below illustrates.

Trend traders will want to observe that the strongest trend appears on the 14 day horizon; over that time period, price has been moving up. For additional context, note that price has gone up 11 out of the past 14 days. As for those who trade off of candlesticks, we should note that were seeing pin bar pattern appearing here.

Behold! Here are the top tweets related to Bitcoin:

GOLD about to pass $2000 all time high. BITCOIN pass $10,000. Why silver best. Silver supplies low. Silver used in tech, EVs medicine, water purification & money. Stock market about to crash. Silver at $25 below high of $45. Everyone can afford. Please do not miss opportunity.

Good news: Gold, Silver, #Bitcoin all hitting, or going, to new ATH Bad news: Its because global central banks are staging a debt-for-equity coup disenfranchising 7.6 billion people who will be left for dead unless they have some Gold, Silver, Bitcoin

Dont know why I said deleted lolI meant abandoned, because fuck that thingWouldnt want to rid you of the Sonic 06 series eitherthat and I could do with whatever bitcoin that thing can chuck out to keep the lights onHaving 11 cats and no brain cells doesnt come cheap

As for a news story related to Bitcoin getting some buzz:

Bitcoin. Thanks, but no thanks.. A brief history of disruption. | by Tyler Durden | The Crypto | Medium

Henry Ford had produced one of the modern worlds most important innovations and revolutionized American society and later, the whole world.American computer engineer Ray Tomlinson was working with his team on the development of an early computer operating system and he created two programs called SNDMSG and READMAIL.Steve Jobs, a modern day visionary imagined a future in which every home had a personal computer and Apple Computer, Inc.This was preceded by several years of strenuous campaigning by Sir Tim, now 62, to persuade professors, convince students, urge programmers and computer enthusiasts to create and build more servers and web browsers.Wed existed wonderfully for hundreds of years without the single most important technology of the 21st century coming along and creating all this new opportunity.Satoshi Nakamoto, an anonymous computer programmer produced and published a paper on a cryptography mailing list.

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Bitcoin (BTC) Down $78.19 Over Past 4 Hours, Moves Up For the 2nd Day In A Row; Pin Bar Pattern Appearing on Chart - CFDTrading

Federal Court Can’t See Any First Amendment Implications In Local Ordinance Blocking The Photography Of Children – Techdirt

from the I-guess-a-law-is-good-if-it-makes-something-illegal dept

You can't always pick your fighter for Constitutional challenges. Sometimes you're handed an unsympathetic challenger, which makes defending everyone's rights a bit more difficult because a lot of people wouldn't mind too much if this particular person's rights are limited. But that's not how rights work.

A pretty lousy decision has been handed down by a Minnesota federal court. A challenge of two laws -- one city, one state -- has been met with a judicial shrug that says sometimes rights just aren't rights when there are children involved. (h/t Eric Goldman)

The plaintiff is Sally Ness, an "activist" who appears to be overly concerned with a local mosque and its attached school. Ness is discussed in this early reporting on her lawsuit, which shows her activism is pretty limited in scope. Her nemesis appears to be the Dar Al-Farooq Center and its school, Success Academy. Ness feels there's too much traffic and too much use of a local public park by the Center and the school.

Here's how she's fighting back against apparently city-approved use of Smith Park:

Ness has taken it upon herself to document activity at site. That includes maintaining a public blog and Facebook page all about the DAF/Success Academy controversy, complete with photos and video of street traffic, kids being dropped off at school, and people otherwise going about their business.

Her legal representation in this lawsuit isn't that sympathetic either.

The American Freedom Law Center, which claims that the battle for Americas soul is being waged in the courtrooms across America against secular progressives and Sharia-advocating Muslim Brotherhood interests, is co-counseling the case. The Southern Poverty Law Center calls that organizations co-founder David Yerushalmi an anti-Muslim activist and a leading proponent of the idea that the United States is threatened by the imposition of Muslim religious law, known as Shariah.

Her lawyer says this has nothing to do with the school's religious affiliation. Her co-counsel, David Yerushalmi, disagrees.

In a statement, he says Ness predicament is just another example of encroachment on our liberties when Islam is involved.

Ness became involved when the mosque opened its school and obtained a Conditional Use Permit for Smith Park that allowed students to use it during school days. Ness believes the permit is being violated on a daily basis by students' "excessive" use of park facilities that makes it "impossible" for nearby residents to use it at the same time.

To document these supposed violations, Ness has approached children in the park and parked across the street to take photographs/record DAF students using the park. She had two run-ins with local law enforcement before filing her lawsuit. After the most recent law enforcement encounter, Bloomington police attempted to charge Ness with felony harassment, but the Hennepin County Attorney's office declined to bring charges against her. Bloomington prosecutors also declined to prosecute Ness.

Ness sued, claiming the laws cited infringed on her Constitutional rights and that the ongoing threat of prosecution has resulted in her curtailing her documentation of park use by the school.

The problem is the laws. Ness' behavior is problematic but it shouldn't be criminally problematic. First, the state's harassment law -- as quoted in the court's opinion [PDF] -- does not require prosecutors to prove intent.

Subdivision 1. Definition. As used in this section, harass means to engage in conduct which the actor knows or has reason to know would cause the victim under the circumstances to feel frightened, threatened, oppressed, persecuted, or intimidated, and causes this reaction on the part of the victim regardless of the relationship between the actor and victim.

Subd. 1a. No proof of specific intent required. In a prosecution under this section, the state is not required to prove that the actor intended to cause the victim to feel frightened, threatened, oppressed, persecuted, or intimidated, or except as otherwise provided in subdivision 3, paragraph (a), clause (4), or paragraph (b), that the actor intended to cause any other result.

Then there's an additional ordinance -- one put in place by the city of Bloomington after Ness' two run-ins with the local PD -- that criminalizes Ness' documentation of park activities.

(24) No person shall intentionally take a photograph or otherwise record a child without the consent of the child's parent or guardian.

This is amazingly broad. It criminalizes journalism and the recording of criminal acts by minors. This revision appears to have been crafted solely to target Ness and her activism. Ness was also a frequent commenter at Bloomington city council meetings until filing this lawsuit.

The court says Ness has no standing to challenge the laws. According to the judge, she does not face a credible threat of prosecution. The decision cites the two refusals to prosecute, as well as prosecutors' statements on the issue.

Ness claims she intends to monitor an issuethe non-compliant use of DAFs facilities and the use of Smith Parkby filming and photographing the activity in the physical vicinity of DAF, which may include filming and photographing people. Compl. 36, 47, 70, 71; Ness Decl. 6, 18, 28. Ness does not claim a desire to surveil individuals or track their location by filming or photographing them once they leave DAFs neighborhood. As Ness herself has stated, I try to make this as not about people . . . . Its not specifically about an individual. Its about the City collectively not doing their job. Jones Decl. Ex. 1 at 18:4918:53. Thus, as the County Attorney and the City both acknowledge, Ness intended conduct is not proscribed by the Harassment Statute because she is not tracking or monitoring a particular individual.

But then the court goes on to quote police officers' implicit threats of arrest as evidence Ness won't be subjected to further law enforcement scrutiny or prosecution.

Ness relies on the police report from the incident, which states that Officer Meyer asked [Ness] to stop filming, and that Ness was advised that she could be charged with harassment if the parents and principal felt intimidated by her actions. Compl. 54. However, the bodycam footage of the encounter establishes that Sgt. Roepke expressly told Ness this is a public place, . . . you have a right to . . . take pictures in a public place or video or, or anything like that. Theres not an issue with that. . . . [B]ut if youre doing it in a means to intimidate them or to harass them, then it becomes a problem. Jones Decl. Ex. 3 at 1:50. Sgt. Roepke also told Ness if you want to take some pictures, come and take some pictures and then move on. Id. at 7:50. When Ness described the August 2019 encounter to Detective Bloomer months later during her interview, Ness stated that Sgt. Roepke clarified Ness conduct was not harassing behavior, and told her to be careful and read the statute. Jones Decl. Ex. 5 at 36:2236:43. The police report of the August 2019 incident, particularly when viewed together with Sgt. Roepkes statements and Ness own recollection of the incident, does not rise to the level of a credible threat of prosecution. Ness decision to chill her speech, after being told by Sgt. Roepke that she had a right to take videos and that her conduct was not harassing behavior, was not based on an objectively reasonable fear of prosecution.

Unfortunately, this supposedly "unreasonable" fear of prosecution stems directly from the law, making it a lot more reasonable than the court says. Prosecutors do not have to prove intent. And, as the officer stated clearly, all it would take is for subjects of Ness' recordings to feel harassed. It doesn't matter whether or not Ness intended to harass anyone. That's pretty open-ended and that makes her fear of prosecution a lot more reasonable.

The court agrees Ness has standing to sue the city of Bloomington over its ban on filming children.

The City Defendants argue that [e]ven if Ness had standing to sue, her facial challenge to the ordinance under the first Amendment would fail. City Defs. Mem. Supp. Mot. Dism. [Docket No. 68] at 10 (emphasis added). However, the City Defendants briefing does not include an argument for why Ness might lack standing to challenge the City Ordinance. Ness intended conduct will include photographing and filming children in a City park without parental consent. This conduct is proscribed by the City Ordinance, and the City has not disavowed an intent to charge Ness with violating the City Ordinance if she were to engage in this conduct. Under these circumstances, Ness decision to chill her speech due to the existence of the City Ordinance is objectively reasonable. Ness has standing to challenge the City Ordinance.

But it says she has nothing to sue about because the ordinance does not affect her First Amendment rights.

Here, the City Ordinance makes no distinction based on who is the photographer or recorder, what use will be made of the photograph or recording, or what message will ultimately be conveyed. Because the limitation on its face does not draw distinctions based on a speakers message or viewpoint, it is content neutral.

Neutral, except as to the content of the recordings, which is what's targeted by the city's ban. But the court says the definition of "content" hinges on what the speech conveys, rather than what it contains.

Ness also points out the ordinance is unconstitutional because it fails to do what it purports to do: protect children from being recorded. The court disagrees, saying the ordinance is adequate enough to achieve its aims.

Ness argues that the City Ordinance is underinclusive because if a person takes a step outside a City park and films children from the street, the City Ordinance will not be violated. Ness contends this underinclusiveness undermines the Citys claimed interest in protecting childrens privacy and preventing them from being exploited or intimidated. However, requiring would-be recorders to collect images from a distance, rather from inside a City park, makes it less likely that a child in the park will feel frightened or that the childs identity will be ascertainable. Thus, the Citys important government interest in protecting children is not undermined by allowing a person to record children from just outside a City parks boundaries.

Finally, the judge says the ends justify the means. The judge appears to believe laws are "narrowly tailored" if they accomplish what they set out to do.

As discussed above, the City Ordinance promotes the important government interest in regulating the competing uses of City parks and protecting childrens privacy and sense of safety and freedom from intimidation while playing in a City park. This interest would be achieved less effectively without the City Ordinance. The City Ordinance is narrowly tailored.

Sure, and the city's attempts to achieve other interests would undoubtedly be more effective if the Constitution didn't exist. But it does. And the court is supposed to be a check against government overreach, not an enabler of government efficiency.

The lawsuit is dismissed. The court says Ness can film kids from outside of the park's boundaries without fear of prosecution. Of course, this is what Ness was doing when she was approached by officers who told her to "take her photos" and "move on." Even if Ness complies with the terms of the ordinance the city appears to have passed just to stop her from doing what she was doing, she still faces the possibility of being subjected to further police action. And even if prosecutors refuse to press charges, there's still the hassle of the arrest, and the loss of time and freedom during the detainment. These harms aren't imaginary. The law written to make it more difficult for one Bloomington resident to engage in documentation of perceived permit violations stays on the books.

Most people will probably be fine with this outcome. After all, it mainly affects someone whose interest in park usage seems to be primarily motivated by bigotry. This is all but confirmed by her choice (or acceptance) of the American Freedom Law Center's legal representation. But bad people can still raise legitimate Constitutional complaints. This isn't a victory for Bloomington. It's a loss for its residents who are subject to a badly written law. Even if they have no desire to violate the ordinance, the law can still be wielded against citizens engaged in legitimate activities (like news gathering), thanks to this court's support.

Filed Under: 1st amendment, activist, children, free speech, photography, privacy, sally ness

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Federal Court Can't See Any First Amendment Implications In Local Ordinance Blocking The Photography Of Children - Techdirt

Fired Tiverton teacher Amy Mullen gets her job back after judges ruling – newportri.com

"Never once was her teaching called into question," U.S. District Court Chief Justice John J. McConnell Jr. said.

Amy Mullen has her job back with the Tiverton School Department.

U.S. District Court Chief Justice John J. McConnell Jr., ruling on a preliminary injunction Friday morning, said Mullens First Amendment rights were violated when she was terminated from her teaching job April 15 for speaking up about wanting to discuss distance learning as it pertained to her member teachers. Mullen is head of the teachers union.

In granting the preliminary injunction filed by attorney Elizabeth Wiens, the judge ordered that Mullen "be restored as a teacher until further notice. No doubt Ms. Mullen was retaliated against because of her First Amendment speech," McConnell said from the bench at the end of a virtual hearing.

"Never once was her teaching called into question" in the 25 years Mullen has worked for the district as a special education teacher, McConnell said, adding that she is considered "an exemplary teacher."

Four attorneys were representing the School Department two were from the Interlocal Trust, the insurance carrier for the town. There was no ruling on their motion to dismiss the case Mullen brought against the district.

McConnell said he would take the motion to dismiss under advisement "but at least part will be denied," he said. He said he was "bothered" by the individual suits against individual members of the School Committee who voted to terminate Mullen on the recommendation of Superintendent Peter Sanchioni.

"Shell be back on the payroll as of today," School Committee attorney Stephen Robinson said Friday afternoon. "We clearly will respect the courts orders," Robinson said, but added they "respectfully disagree with the judge in his findings of fact and conclusions of law. We will explore our options."

Wiens wrote in the motion for preliminary injunction that Mullen should be reinstated immediately, noting in the 15-page motion that less than two years ago, the U.S. Supreme Court held that "union speech is overwhelmingly of substantial public concern."

Wiens also wrote that "there can be no doubt that speech relating to public education, including the creation of a Distance Learning Plan for students during a global pandemic is a matter of public concern." Mullens speech, Wiens wrote, "was the sole factor" in her termination.

In providing background, Wiens said Sanchioni "repeatedly violated the collective bargaining agreement" between the School Department and NEA-Tiverton, and because of numerous grievances and unfair labor practice complaints filed by the union, there was "animus towards Mullen."

On March 12, 2020, Mullen attended a professional development committee meeting and communicated to the superintendent that online learning plans need to be negotiated with the union. She learned of a March 18 meeting for a distance learning plan and she arrived early to say the union should be part of the discussion.

"Sanchioni raised his voice, told Mullen she was not invited to the meeting and told her he would write her up for insubordination if she did not leave," according to the motion.

She was placed on paid administrative leave on March 21 and told to cease and desist all communications with parents, teachers and administrators, or there would be further discipline, Wiens wrote. An April 6 letter to Mullen from the superintendent notified her of his intent to recommend to the School Committee that she be suspended without pay for "her persistent disruption and insubordination." A Facebook post she made "violated the gag order," it was later charged.

The School Committee voted unanimously April 14 to terminate Mullen, but voted again at a meeting in May to suspend without pay and terminate her at the end of the 2020-2021 school year. That vote was 4-0, with committee member Sally Black abstaining. Voting in favor was Chairman Jerome Larkin, Vice Chairwoman Diane Farnworth, Deborah Pallasch and Elaine Pavao.

Mullen filed suit soon after her termination, saying she was retaliated against by the district for speaking on behalf of her union members. The district said she was terminated for "unprofessional and disruptive behavior."

In a June 17 email to Mullen, who wanted to be on the School Reopening Committee as a representative of the teachers union, she was advised by School Department legal counsel that she was not allowed on school property and not permitted to speak with school staff or administrators because of her suspension, Wiens wrote.

Much of the discussion at the hearing Friday morning centered on whether Mullen was speaking as an employee of the district, or as a private citizen at the distance learning meetings.

"The speech took place in the workplace. It had to do with work-related issues," said attorney Marc DeSisto, representing the Interlocal Trust. "When a union president speaks, that union official is speaking in a workplace official capacity and not as a private citizen," DeSisto argued, saying union and public employee "are symbiotic."

He said the court "was crossing out union and making it outside the employee realm. "It goes back to whether she spoke as a private citizen" and was protected by First Amendment speech, or spoke as an employee subject to disciplinary action.

Wiens told the judge that "every court has found that speech as a union representative is not speech as an employee. We allege Amy Mullen was terminated because of her association with the union. The reason for the termination was her status as union president."

Wiens also argued the Tiverton School Department and the individual members of the School Committee who voted to terminate her and thus violated her First Amendment rights, should be held liable for damages for violating the Constitution.

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Fired Tiverton teacher Amy Mullen gets her job back after judges ruling - newportri.com

Hanford speaks up: Recently published Letters to the Editor – Hanford Sentinel

Are you Impressed with American Marxism? We have had, over the past three months, a free trial: Seattle CHOP, Portland CLAT, D.C. CHAZ and U.S. cities. All demonstrated Marxist first principles targeting property and family.

Benefiting none, they destroyed private property and demonstrated the tyranny of a Centrally Controlled State. Other Marxist activism created burned out war zones in major U.S. cities worse than in Iraq or Afghanistan.

Where are the reasonable representatives of we the people?

Its who is leading this destruction, staying far away but fanning the flames! Opal Tometi, a Nigerian-born-U.S. educated activist; Patrisse Cullors with Alice Garza, both LGBTQ Activist a.k.a. Black Lives Matter founders. Who assists them? Eric Mann former 1960s Weather Underground/SDS leader who advocates violence having served prison time for violence.

They embody another Marxist philosophy.

From their website:

We disrupt the Western-prescribed nuclear family structure requirement by supporting each other as extended families and villages that collectively care for one another, especially our children

We build a space that affirms Black women [not men] and is free from sexism, misogyny, and environments in which men are centered.

This is a rather contrary concept given Martin Luther Kings statement on family:

The group consisting of mother, father and child is the main educational agency of mankind.

When did family become the enemy? When Marx required child raising by the State to ensure compliance and obedience in the future. Not much different from 1935 Germanys absolute child control or current government calls to get children into pre-school before age three.

Tyranny uses many names; its goals never change from absolute power at any price or lie. Consider BLM Principle No. 3., Loving Engagement stating We are committed to embodying and practicing justice, liberation, and peace in our engagements with one another.

There hasnt been much Loving Engagement in CHOP, CLAT, CHAZ or any major city by Black Lives Matter affirming. BLM is a ghost when help is needed. Remember your panic about no toilet paper? What happens when you cant find a cop? Need I say more?

Gary Smith

Lemoore

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Hanford speaks up: Recently published Letters to the Editor - Hanford Sentinel

What Robots Need to Succeed: Machine-Learning to Teach Effectively – Robotics Business Review

With machine learning, algorithms are automatically generated from large datasets, speeding the development and reducing the difficulty of creating complex systems, including robotics systems. While data at scale is what makes accurate machine learning go, the data used to train ML models must also be very accurate and of high quality.

By Hyun Kim | July 31, 2020

The Mid-twentieth century sociologist David Reisman was perhaps the first to wonder with unease what people would do with all of their free time once the encroaching machine automation of the 1960s liberated humans from their menial chores and decision-making. His prosperous, if anxious, vision of the future only half came to pass however, as the complexities of life expanded to continually fill the days of both man and machine. Work alleviated by industrious machines, such as robotics systems, in the ensuing decades only freed humans to create increasingly elaborate new tasks to be labored over. Rather than give us more free time, the machines gave us more time to work.

Machine LearningToday, the primary man-made assistants helping humans with their work are decreasingly likely to take the form of an assembly line of robot limbs or the robotic butlers first dreamed up during the era of the Space Race. Three quarters of a century later, it is robotic minds, and not necessarily bodies, that are in demand within nearly every sector of business. But humans can only teach artificial intelligence so much or at least at so great a scale. Enter Machine Learning, the field of study in which algorithms and physical machines are taught using enormous caches of data. Machine learning has many different disciplines, with Deep Learning being a major subset of that.

Today Deep Learning is finally experiencing its star turn, driven by the explosive potential of Deep Neural Network algorithms and hardware advancements.

Deep Learning ArrivesDeep Learning utilizes neural network layers to learn patterns from datasets. The field was first conceived 20-30 years ago, but did not achieve popularity due to the limitations of computational power at the time. Today Deep Learning is finally experiencing its star turn, driven by the explosive potential of Deep Neural Network algorithms and hardware advancements. Deep Learning require enormous amounts of computational power, but can ultimately be very powerful if one has enough computational capacity and the required datasets.

So who teaches the machines? Who decides what AI needs to know? First, engineers and scientists decide how AI learns. Domain experts then advise on how robots need to function and operate within the scope of the task that is being addressed, be that assisting warehouse logistics experts, security consultants, etc.

Planning and LearningWhen it comes to AI receiving these inputs, it is important to make the distinction between Planning and Learning. Planning involves scenarios in which all the variables are already known, and the robot just has to work out at what pace it has to move each joint to complete a task such as grabbing an object. Learning on the other hand, involves a more unstructured dynamic environment in which the robot has to anticipate countless different inputs and react accordingly.

Learning can take place via Demonstrations (Physically training their movements through guided practice), Simulations (3D artificial environments), or even by being fed videos or data of a person or another robot performing the task it is hoping to master for itself. The latter of these is a form of Training Data, a set of labeled or annotated datasets that an AI algorithm can use to recognize and learn from. Training Data is increasingly necessary for todays complex Machine Learning behaviors. For ML algorithms to pick up patterns in data, ML teams need to feed it with a large amount of data.

Accuracy and AbundanceAccuracy and abundance of data are critical. A diet of inaccurate or corrupted data will result in the algorithm not being able to learn correctly, or drawing the wrong conclusions. If your dataset is focused on Chihuahuas, and you input a picture of a blueberry muffin, then you would still get a Chihuahua. This is known as lack of proper data distribution.

Insufficient training data will result in a stilted learning curve that might not ever reach the full potential of how it was designed to perform. Enough data to encompass the majority of imagined scenarios and edge cases alike is critical for true learning to take place.

Hard at WorkMachine Learning is currently being deployed across a wide array of industries and types of applications, including those involving robotics systems. For example, unmanned vehicles are currently assisting the construction industry, deployed across live worksites. Construction companies use data training platforms such as Superb AI to create and manage datasets that can teach ML models to avoid humans and animals, and to engage in assembling and building.

In the medical sector, research labs at renowned international universities deploy training data to help computer vision models to recognize tumors within MRIs and CT Scans. These can eventually be used to not only accurately diagnose and prevent diseases, but also train medical robots for surgery and other life-saving procedures. Even the best doctor in the world has a bad nights sleep sometimes, which can dull focus the next day. But a properly trained robotic tumor-hunting assistant can at perform peak efficiency every day.

Living Up to the PotentialSo whats at stake here? Theres a tremendous opportunity for training data, Machine Learning, and Artificial Intelligence to help robots to live up to the potential that Reisman imagined all those decades ago. Technology companies employing complex Machine Learning initiatives have a responsibility to educate and create trust within the general public, so that these advancements can be permitted to truly help humanity level up. If the world can deploy well-trained, built and purposed AI, coupled with advanced robotics, then we may very well live to see some of that leisure time that Reisman was so nervous about. I think most people today would agree that we certainly could use it.

Hyun Kim, Co-founder and CEO, Superb AI

Hyunsoo (Hyun) Kim is the co-founder and CEO of Superb AI, and is on a mission to democratize data and artificial intelligence. With a background in Deep Learning and Robotics during his PhD studies at Duke University and career as a Machine Learning Engineer, Kim saw the need for a more efficient way for companies to handle machine learning training data. Superb AI enables companies to create and manage the enormous amounts of data they need to train machine learning algorithms, and lower the hurdle for industries to adopt the technology. Kim has also been selected as the featured honoree for the Enterprise Technology category of Forbes 30 Under 30 Asia 2020, and Superb AI managed last year to join Y Combinator, a prominent Silicon Valley startup accelerator.

Excerpt from:
What Robots Need to Succeed: Machine-Learning to Teach Effectively - Robotics Business Review

How one company is using machine learning to remove bias from the hiring process – WRAL Tech Wire

Editors note: Stuart Nisbet is chief data scientist at Cadient Talent, a talent acquisition firm based in Raleigh.

RALEIGH At Cadient Talent, its a question that we wrestle with on a daily basis: How do we eliminate bias from the hiring process?

The only way to address a problem or bias is to acknowledge it head on, under the scrutiny of scientific examination. Through the application of machine learning, we are able to learn where we have erred in the past, allowing us to make less biased hiring decisions moving forward. When we uncover unconscious bias, or even conscious bias, and educate ourselves to do better based on unbiased machine learning we are able to take the first step toward correcting an identified problem.

Bias is defined as a prejudgment or a prejudice in favor of or against one thing, person, or group compared with another, usually in a way that is considered to be unfair. Think of bias as three sets of facts: The first is a set of objective facts that are universally accepted. The second is a set of facts that confirms beliefs, in line with what an individual believes to be true. Where bias enters the picture is in the intersection between the objective facts and the facts that confirm personal beliefs.

By selectively choosing the facts that confirm particular beliefs and focusing on the things that confirm those beliefs, bias enters. If we look at hiring from that perspective, and if our goal is to remove bias from the hiring process, then we need to remove the personal choice of which data points are included in the process. All data points that contribute to a positive choice (hire the applicant) or negative choice (decline the applicant) are included in the process and choosing the data points and their weights is done objectively through statistics, not subjectively through human choice.

How can computer algorithms help us do this? Our goal is to be able to augment the intelligence of humans, in particular by using the experiences and prior judgment in past hiring decisions, with an emphasis on those that resulted in good hiring decisions. Good hiring can be measured in a number of ways, that dont implement inappropriate bias, such as the longevity of employees. If a new hire does not remain on the job very long, then perhaps the recruiting effort was not done well, and, in hindsight, you would not have chosen that applicant. But, if you hire someone who is productive and stays for a long time, that person would be considered a good hire.

We want to remove bias when it is unintentional or has no bearing on whether an employee is going to be able to perform the job in a satisfactory manner. So, if a hiring managers entire responsibility is to apply their knowledge and experience to determine the best fit, why do we use machine learning to eliminate bias? Because, artificial intelligence only removes the bias towards non-work-related candidate attributes and augments decisions based on relevant work traits, where there is appropriate bias.

Our goal is then to make the hiring process as transparent as possible and consider all of the variables that are used in a hiring decision. Thats extremely complicated, if not impossible, if you have nothing but a human-based approach because the decision-making of a hiring manager is far more complex and less understood than those of a machine learning algorithm. So, we want to focus on the strength of simplicity in a machine learning algorithm; meaning we only want to look at variables, columns, and pieces of data in the algorithm that are pertinent to the hiring process and do not include any data points that are not relevant to performance.

Stuart Nisbet

An assessment result, for example, whether cognitive or personality-based, may be a very valid data point to consider if the traits being assessed are pertinent to the job. Work history and demonstrated achievement in similar roles may be very important to consider. The opposite is very clear, too. Gender, ethnicity, and age should have no legitimate bearing on someones job performance. This next point is critical. A hiring manager cannot meet an applicant in an interview and credibly say that they dont recognize the gender, ethnicity, or general age category of the person sitting across from them. No matter our intentions, this is incredibly hard to do. Conversely, it is the easiest task for an algorithm to perform.

If the algorithm is not provided gender, ethnicity, or age, there is no chance for those variables to be brought into the hiring decision. This involves bringing in the data that is germane, having a computer look at what hiring decisions have been made in the past that have resulted in high performing long-term employees, and then strengthening future decisions based on the past performance of good hiring management practices. This will ultimately remove the bias in hiring.

One of the things that deserves consideration is the idea of perpetuating past practices that could be biased. If all we are doing is hiring like we have hired in the past and there have been prejudicial or biased hiring practices, that could promote institutional bias. Through time, we have trained computers to do exactly what a biased manager would have done in the past. If the only data that is used (trained) for hiring is the same data that is selected by biases of the past, then it is difficult to train on data that is not biased. For example, if we identify gender as a bias in the hiring process, and we take the gender variable out of the algorithm, gender would not be considered. When we flag previous bias, we are able to minimize future bias.

We should unabashedly look at whether we are able to identify and learn from hiring practices that may have had bias in the past. This is one of the greatest strengths of applying very simple machine learning algorithms in the area of hourly hiring.

An aspect of the hiring process that opens up a lot of opportunities in the area of artificial intelligence and machine learning is implementing diversity.

Artificial intelligence can really differentiate itself here. Machine learning can make the very best hiring decisions based on the data that its given; if you have diversity goals and want hiring practices to encourage a diverse work population, it is very simple to choose the best candidates from whichever populations are important to corporate goals. This can be done transparently and simply. It doesnt prioritize one person over another. It allows the hiring of the very best candidates from each population that youre interested in representing the company.

Upon scrutiny and scientific examination, machine learning can be a very valuable tool for augmenting the hiring decisions managers make every day and help to understand when bias has entered into our decisions and yielded far less than our collective best.

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How one company is using machine learning to remove bias from the hiring process - WRAL Tech Wire

MSPs are Bolstering Security Programs with Machine Learning and Automation – Channel Futures

Overcome the skills shortage and alert fatigue with advanced machine learning and automation technology.

Advanced threats, a shortage of security experts and the rise in work-from-home together form a catalyst for MSPs to enhance cybersecurity effectiveness for their customers. As MSPs seek ways to increase efficiency and do more with less, theyre turning to advanced analytical capabilities like machine learning, security analytics and automation. All of these have moved past their initial hype cycle and are now adopted and delivering enhanced ROI and outcomes in IT and cybersecurity.

The future of your business is Big Data and Machine Learningtied to the business opportunities and customer challenges before you.

Eric Schmidt, then CEO of GoogleCloudNext Conference in 2017

Machine learning and automation are more than popular buzzwords in the cybersecurity industry. These analytic capabilities make sense of large volumes of raw data to create context and find unknown attacks that speed up decision making. When combined with cybersecurity experts, they hold real promise for their ability to transform IT and security operations for organizations of all sizes. While not a magic potion that instantly perfects data security, these advanced tools offer MSPs a way to augment limited staff in the ongoing battle against cyber criminals.

The Value of Machine Learning and Automation in Cybersecurity

With digital transformation serving as a catalyst for larger volumes of data and technology, use cases for ML and automation in IT and security operations are growing. While not exhaustive, key use cases include:

Analyzing vast reams of data for suspicious activity: Its challenging to process billions of logs with an all-manual approach. Machine learning does the initial correlation work to process incoming log streams, reduce false positives and alert security operations center (SOC) analysts who perform a second level of triage and potential threat hunting.

Improving SOC efficiency and effectiveness: Machine learning and automation manage repetitive and potentially error-prone tasks that can overwhelm security teams. The result is higher job satisfaction and retention of hard-to-find cybersecurity professionals.

Increasing speed, accuracy and scale of threat detection: Automated incident response can launch a set of corrective actions, open a ticket for SOC triage and even block suspicious processes. Faster detection and remediation reduce the potential damage of attackers.

Detecting anomalous behavior by users and supply chain partners: Detect insider threats and advanced attacks with machine learning to understand and predict normal baseline system activity and identify exceptions that signal a cybersecurity risk. A SIEM (security information and event management) solution provides user and entity behavior analysis (UEBA) to detect insider threats, lateral movement and advanced attacks.

Through advancements and adoption of machine learning and security automation, MSPs are harnessing the vast reams of device and client data to foster better cyber decision making.

Cyber Criminals Also Embrace Advanced Tools

Defenders arent the only ones looking at emerging technologies. Global cybercrime damages are predicted to reach $6 trillion annually by 2021, according to the2019 Annual Cybercrime Report by Cybersecurity Ventures. Cybercriminals are upping their game to use the latest tools and technology to improve outcomes for their exploits. Hackers are using

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MSPs are Bolstering Security Programs with Machine Learning and Automation - Channel Futures

Going Deeper with Data Science and Machine Learning – Database Trends and Applications

Surviving and thriving with data science and machine learning means not only having the right platforms, tools and skills, but identifying use cases and implementing processes that can deliver repeatable, scalable business value.

However, the challenges are numerous, from selecting data sets and data platforms, to architecting and optimizing data pipelines, and model training and deployment.

In response, new solutions have emerged to deliver key capabilities in areas including visualization, self-service, and real-time analytics. Along with the rise of DataOps, greater collaboration, and automation have been identified as key success factors.

DBTA recently hosted a special roundtable webinar featuring Alyssa Simpson Rochwerger, VP of AI and data, Appen; Doug Freud, SAP platform and technology global center of excellence, VP of data science; and Robert Stanley, senior director, special projects, Melissa Informatics, who discussed new technologies and strategies for expanding data science and machine learning capabilities.

According to a Gartner 2020 CIO survey, only 20% of AI projects deploy, Rochwerger said. The top challenges are skills of staff, understanding the benefits and uses of AI, and the data scope and quality.

She said businesses need to start out by clarifying a goal so they can then know where the data is coming from. Once organizations know where the data is coming from, they can find and fill in the gaps. Having a diverse team of humans can make it easier to sift and combine data.

According to Data2020: State of Big Data Study Regina Corso Consulting 2017, 86% of companies arent getting the most out of their data and they are limited by data complexity and sprawl, Freud explained.

SAP Data Intelligence can meet companies in the middle, Freud said. The platform boasts that its enterprise AI meets intelligent information management.

The platform features benefits that include:

Stanley took another approach by introducing the concept of data quality (DQ) fundamentals with AI. AI can be useful for DQ, particularly with unstructured or more complex data, bringing competitive advantage.

Using AI (MR and ML), more efficient methods for identification, extraction and normalization has been developed. AI on clean data enables pattern recognition, discovery and intelligent action.

Machine reasoning (MR) relies on knowledge captured and applied within ontologies using graph database technologies - most formally, using SDBs, he explained.

Machine reasoning can make sense out of incomplete or noisy data, making it possible to answer difficult questions. MR delivers highly confident decision-making by applying existing knowledge and ontology-enable logic to data, Stanley noted.

An archived on-demand replay of this webinar is available here.

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Going Deeper with Data Science and Machine Learning - Database Trends and Applications

An automated health care system that understands when to step in – MIT News

In recent years, entire industries have popped up that rely on the delicate interplay between human workers and automated software. Companies like Facebook work to keep hateful and violent content off their platforms usinga combination of automated filtering and human moderators. In the medical field, researchers at MIT and elsewhere have used machine learning to help radiologistsbetter detect different forms of cancer.

What can be tricky about these hybrid approaches is understanding when to rely on the expertise of people versus programs. This isnt always merely a question of who does a task better; indeed, if a person has limited bandwidth, the system may have to be trained to minimize how often it asks for help.

To tackle this complex issue, researchers from MITs Computer Science and Artificial Intelligence Lab (CSAIL) have developed a machine learning system that can either make a prediction about a task, or defer the decision to an expert. Most importantly, it can adapt when and how often it defers to its human collaborator, based on factors such as its teammates availability and level of experience.

The team trained the system on multiple tasks, including looking at chest X-rays to diagnose specific conditions such as atelectasis (lung collapse) and cardiomegaly (an enlarged heart). In the case of cardiomegaly, they found that their human-AI hybrid model performed 8 percent better than either could on their own (based on AU-ROC scores).

In medical environments where doctors dont have many extra cycles, its not the best use of their time to have them look at every single data point from a given patients file, says PhD student Hussein Mozannar, lead author with David Sontag, the Von Helmholtz Associate Professor of Medical Engineering in the Department of Electrical Engineering and Computer Science, of a new paper about the system that was recently presented at the International Conference of Machine Learning. In that sort of scenario, its important for the system to be especially sensitive to their time and only ask for their help when absolutely necessary.

The system has two parts: a classifier that can predict a certain subset of tasks, and a rejector that decides whether a given task should be handled by either its own classifier or the human expert.

Through experiments on tasks in medical diagnosis and text/image classification, the team showed that their approach not only achieves better accuracy than baselines, but does so with a lower computational cost and with far fewer training data samples.

Our algorithms allow you to optimize for whatever choice you want, whether thats the specific prediction accuracy or the cost of the experts time and effort, says Sontag, who is also a member of MITs Institute for Medical Engineering and Science. Moreover, by interpreting the learned rejector, the system provides insights into how experts make decisions, and in which settings AI may be more appropriate, or vice-versa.

The systems particular ability to help detect offensive text and images could also have interesting implications for content moderation. Mozanner suggests that it could be used at companies like Facebook in conjunction with a team of human moderators. (He is hopeful that such systems could minimize the amount of hateful or traumatic posts that human moderators have to review every day.)

Sontag clarified that the team has not yet tested the system with human experts, but instead developed a series of synthetic experts so that they could tweak parameters such as experience and availability. In order to work with a new expert its never seen before, the system would need some minimal onboarding to get trained on the persons particular strengths and weaknesses.

In future work, the team plans to test their approach with real human experts, such as radiologists for X-ray diagnosis. They will also explore how to develop systems that can learn from biased expert data, as well as systems that can work with and defer to several experts at once.For example, Sontag imagines a hospital scenario where the system could collaborate with different radiologists who are more experienced with different patient populations.

There are many obstacles that understandably prohibit full automation in clinical settings, including issues of trust and accountability, says Sontag. We hope that our method will inspire machine learning practitioners to get more creative in integrating real-time human expertise into their algorithms.

Mozanner is affiliated with both CSAIL and the MIT Institute for Data, Systems and Society (IDSS). The teams work was supported, in part, by the National Science Foundation.

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An automated health care system that understands when to step in - MIT News

Today in History | | clintonherald.com – Clinton Herald

Today is Saturday, Aug. 1, the 214th day of 2020. There are 152 days left in the year.

Todays Highlight in History:

On August 1, 1957, the United States and Canada announced they had agreed to create the North American Air Defense Command (NORAD).

On this date:

In 1714, Britains Queen Anne died at age 49; she was succeeded by George I.

In 1907, the U.S. Army Signal Corps established an aeronautical division, the forerunner of the U.S. Air Force.

In 1912, the U.S. Marine Corps first pilot, 1st Lt. Alfred A. Cunningham, went on his first solo flight as he took off in a Burgess/Curtis Hydroplane from Marblehead Harbor in Massachusetts.

In 1914, Germany declared war on Russia at the onset of World War I.

In 1936, the Olympics opened in Berlin with a ceremony presided over by Adolf Hitler.

In 1944, an uprising broke out in Warsaw, Poland, against Nazi occupation; the revolt lasted two months before collapsing.

In 1966, Charles Joseph Whitman, 25, went on an armed rampage at the University of Texas in Austin that killed 14 people, most of whom were shot by Whitman while he was perched in the clock tower of the main campus building. (Whitman, who had also slain his wife and mother hours earlier, was finally gunned down by police.)

In 1981, the rock music video channel MTV made its debut.

In 2001, Pro Bowl tackle Korey Stringer, 27, died of heat stroke, a day after collapsing at the Minnesota Vikings training camp on the hottest day of the year.

In 2007, the eight-lane Interstate 35W bridge, a major Minneapolis artery, collapsed into the Mississippi River during evening rush hour, killing 13 people.

In 2013, defying the United States, Russia granted Edward Snowden temporary asylum, allowing the National Security Agency leaker to slip out of the Moscow airport where he had been holed up for weeks.

In 2014, a medical examiner ruled that a New York City police officers chokehold caused the death of Eric Garner, whose videotaped arrest and final pleas of I cant breathe! had sparked outrage.

Ten years ago: The United States announced that it would provide Pakistan with $10 million in humanitarian assistance in the wake of deadly flooding. Lolita Lebron, a Puerto Rico independence activist whod spent 25 years in prison for participating in a gun attack on the U.S. Congress in 1954, died in San Juan at age 90.

Five years ago: Japans Imperial Household Agency released a digital version of Emperor Hirohitos radio address on Aug. 15, 1945, announcing his countrys surrender in World War II; the digital recording offered clearer audio, although Hirohito spoke in an arcane form of Japanese that many of his countrymen would have found difficult to comprehend.

One year ago: President Donald Trump intensified pressure on China to reach a trade deal by warning he would impose 10% tariffs on Sept. 1 on the remaining $300 billion in Chinese imports that he hadnt already taxed.

We are making critical coverage of the coronavirus available for free. Please consider subscribing so we can continue to bring you the latest news and information on this developing story.

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Today in History | | clintonherald.com - Clinton Herald