Lawmaker is back again with plan to shield records on Kentucky public officers – Courier Journal

Kentucky state Sen. Danny Carroll is back this year with another controversial proposal that would shield personal information about a wide range of "public officers" and their families from public view and would let them sue journalists and otherswho spreadsuch details.

The Kentucky Press Association sharply criticized the legislation this week, saying it's unconstitutional and "a broadside attack on the First Amendment" that violates Kentuckians' due process rights and "will chillthe ability of citizens and journalists alike to speak and write about" public servants.

"It will conceal from the public basic information that has long been available without incident and is essential for citizens to oversee elected and appointed public officials paid with their tax dollars," the KPA's general counsel, Jon Fleischaker and Michael Abate, said in a statement.

"It also will jeopardize the ability of businesses, agenciesand courts to perform routine public functions that depend on the free flow of information regarding public records concerning birth, death, marriage, insurance, property ownership, taxesand political contributions."

Latest: Kentucky lawmakers move to recoup state's $15 million Braidy Industries investment

Carroll's new legislationresembles a hotly debatedbill he filed last year.The legislature approveda modified version ofthat proposal, but Gov. Andy Beshear was able to veto it.

Carroll, R-Benton,was unavailable for comment Wednesday, but an assistant said hehas been working on this legislation with Rep. John Blanton, R-Salyersville. Blanton was involved in modifying Carrolls public records bill last year. He could not immediately be reached for comment.

He also sponsored a highly criticized proposal in 2021that would have made it a crime to insult a law enforcementofficerto the point it could provoke a violent response from them. That bill passed only the Senate, and he recently filed a largely similarbillfor this session.

Carroll's newpublic records legislation, Senate Bill 63, would:

SB 63 also would prohibit public agencies from disclosing "personally identifiable information in records that would reveal the address or location of a public officer" if that person says they don't want that information released.

Amye Bensenhaver, co-director of the Kentucky Open Government Coalition, told The Courier JournalCarroll's latest bill would upend Kentucky's open records law and morph it into a non-disclosure law.

Personal information already is well-shielded by the current law, said Bensenhaver, a former assistant attorney general, adding:"The truth is the privacy exception to theopen records law is so well-developed, and so well-interpreted and understood, that it will in most instances protect this type of information."

More: Gov. Andy Beshear proposes billions for health and human services in 2-year budget plan

She also predicted Carroll's bill would be struck down in court if it becomes law,with taxpayers footing the bill for the legal proceedings."This bill cannot survive any kind of challenge," she said.

Fleischaker and Abate, of the Kentucky Press Association, said the way thisbill is writtencould leadpublic officers to claim basic details, such as their name or employer,must be withheld.

Among other consequences,they said: "This could result inagencies repeatedly withholding public records that have been critically important to exposing egregious abuses by law enforcement officers..."

They also criticized the bill's incredibly broad definition of "immediate family member" as well as the wide array of data the bill classifies as "personally identifiable information."

Fleischaker and Abate raised major concerns as well about how the bill would let public officers and theirso-called immediate family membersfile civil lawsuits against peoplefor posting "personally identifiable information" about them.

Kentucky CRT: Kentucky's anti-'critical race theory' bills draw ire of students, educators

Carroll's bill says someone can be sued for thatif:

"This significant financial liability may be imposed with merely a subjective assertion that the sharing of the information placed the public officer in reasonable fear of physical injury or harm to their property," Fleischaker and Abate said. "Citizens and journalists simply will not know whether they can talk about, or report on, public officials, employees, or controversies that happen to turn on the myriad categories of information protected by the law."

Morgan Watkins is The Courier Journal'schief political reporter. Contact her atmwatkins@courierjournal.com. Follow her on Twitter: @morganwatkins26.

Read more:

Lawmaker is back again with plan to shield records on Kentucky public officers - Courier Journal

‘This witch hunt is personal’: School board votes to censure member in tense New Hanover meeting – StarNewsOnline.com

A New Hanover County Board of Education member said a resolution to censure her wont deter her from her mission to hold those on the board, and in the school district, accountable.

The board passed a resolution to censure member Judy Justice in a 5-2 vote Friday afternoonafter Justice was accused of revealing confidential personnel information to someone who was not permitted to have it. Justice said after the meeting she felt the move was personal, and she plans to continue pushing for more transparency from the district going forward.

Im fighting the battles trying to help the district, and when they fight me, theyre in essence fighting against doing good things for the district, Justice said.

Justice and board Vice Chairwoman Stephanie Walker were the only two members to vote against the censure. A censure does not result in any action it's simply a tool to let Justice know the board does not support or agree with her actions.

Related coverage: New Hanover school board member under scrutiny says censure is 'deflecting from the real issues'

Equity audit: Why is New Hanover Schools looking at canceling contract with Sophic Solutions?

Masks in schools: Some Cape Fear school districts return to masks as COVID-19 cases surge

Kraybill said after the meeting she was made aware of allegations Justice had violated the code of ethics by disclosing personnel information in the fall. The board previously passed a vote of no confidencein June after then-Chairwoman Stefanie Adams accused Justice of lying during a board meeting.

DuringFriday'smeeting, the board went into a closed session to discuss personnel matters that could not be disclosed to the public. After, Justice was given a chance to address the board and thepublic andbrought forth a list of 10 ways other board members had violated the boards code of ethics that had gone unaddressed.

This witch hunt is personal and everyone on this(board)knows it, Justice said during her statement. It is time we did our job for the people and serve the people, not some peoples individual agendas.

Justice also alleged Superintendent Charles Foust had accused her of harassing him. Attorney Colin Shive interrupted Justice, saying he would advise her to move on from that subject to avoid revealing further personnel information. Kraybillsaid the subject was not germane to the topic at hand.

Justice went on to say it was her first amendment right to bring up the accusation and said she had no intention of bringing up confidential personnel information.

As Justice continued her statement about the alleged harassment, Foust interrupted her, saying he had 275 emails to prove she had harassed him.

I will provide emails if thats what you want, Foust said. You cannot and you will not do that.

Call to audience: Is New Hanover school board trying to censor public comments? Chairwoman proposes change.

Cape Fear Academy lawsuit: Here's what you need to know

School sexual abuse case: Sexual assault lawsuit against New Hanover schools end is near: 'It's been a long road'

Justice said the emails she sent him had to do with her asking him to do his job. She said after the meetingFoust has not communicated with her in months, though its district policy that the superintendent communicates regularly with members of the board. She alleged he does not respond to her emails or phonecalls, andsaid thats concerning as shewas elected to represent the public before the school district.

Kraybill quickly called the meeting to recess, and she and Foust went to a separate room to speak with Shive. When they returned, Shive called Justice back and spoke with her for several minutes behind closed doors. Walker also went with Justice to speak with Shive.

When Justice returned, she said she felt the censure vote was taking away from important issues going on in the district, like the continued strain on staff and students from the COVID-19 pandemic and decades of sexual abuse allegations against former teachers and administrators.

Several community members attended the meeting as well, holding signs reading I support Judy and attempting to speak with board members while they recessed.

How is this whats best for the kids? one audience member asked theboard, butdid not receive a response.

Kraybill said after the meeting the vote was not personal, and she hopes the board can be unified moving forward to get to those important topics impacting the district.

The community has been very critical of this board, and boards before us about not being transparent, not handling issues in a timely manner," Kraybill said. When I found out that this had occurred, I just said we need to jump on it and get it resolved.

We've got that behind us, and we should be ready to go,shesaid.

Reporter Sydney Hoover can be reached at 910-343-2339 or shoover@gannett.com.

View post:

'This witch hunt is personal': School board votes to censure member in tense New Hanover meeting - StarNewsOnline.com

Today’s Headlines and Commentary – Lawfare – Lawfare

The Supreme Court has blocked the Biden administration from enforcing a vaccine and testing mandate for large employers, reports the New York Times. The mandate would have required employees either to be vaccinated against the coronavirus or participate in routine testing. The vote on the case was 6 to 3, with Justices Stephen Breyer, Elena Kagan and Sonia Sotomayor in dissent.

The founder and leader of the far-right group known as the Oath Keepers was charged with seditious conspiracy in an investigation into the Jan. 6, 2021 attack on the Capitol, writes the Washington Post. Stewart Rhodes was indicted and arrested after a federal grand jury introduced a new set of charges against a small group of Oath Keepers. According to the indictment, Rhodes and 10 other Oath Keepers are allegedly responsible for organizing a wide-ranging plan to storm the Capitol and disrupt the certification of President Bidens 2020 election victory. Rhodes is the most high-profile person charged in the investigation thus far.

Rep. Kevin McCarthy refused an interview request from the House select committee on Jan. 6, reports the New York Times. On Wednesday, the committee sent McCarthy a formal request to be questioned about his knowledge of former President Trumps state of mind and ongoing conduct in the days following the attack on the Capitol. In a letter to McCarthy, the committee wrote that they are particularly interested in discussions he may have had with Trump about the presidents potential removal or resignation from office. In a statement refusing the interview request, McCarthy wrote that the investigation into Jan. 6 is illegitimate and that the committees only objective is to damage its political opponents.

Democrats in the House passed voting rights legislation that combines the Freedom to Vote Act and the John Lewis Voting Rights Amendment Act, according to the New York Times. Both the Freedom to Vote Act and the John Lewis Rights Amendment Act have previously passed in the House and were denied a vote on the Senate floor. The passing of this repackaged legislation comes the day after President Biden delivered a speech encouraging Democrats to eliminate the 60-vote threshold required to end a filibuster, specifically on voting rights legislation. This will be the Senates fifth time voting on such legislation. The previous four attempts to debate the bills have failed due to Republican filibusters.

The Republican National Committee (RNC) stated that it will require Republican presidential candidates to boycott debates conducted by the Commission on Presidential Debates, reports Politico. The threat of boycotting future presidential debates comes after complaints from Republicans in recent years that the commission is biased against GOP candidates. In a letter to the commission, RNC officials wrote that, the RNC has a duty to ensure that its future presidential nominees have the opportunity to debate their opponents on a level playing field, and that, the RNC will take every step to ensure that future Republican presidential nominees are given that opportunity elsewhere.

On Tuesday, the Cybersecurity and Infrastructure Security Agency, FBI and National Security Agency released a jointcybersecurity advisory, reports The Hill. The advisory outlines tactics, techniques and procedures commonly used by the Russian state, in addition to threat detection actions, guidance on incident response and measures to mitigate cyberthreats. The agencies released the advisory to warn organizations of cyber threats and help the cybersecurity community reduce the risk presented by these threats.

Negotiations with Russia on tensions surrounding Ukraine continue in Vienna at the Organization for Security and Cooperation in Europe (OSCE), writes the Wall Street Journal. Delegations from Ukraine were present at the meeting, marking the first time this week that Kyiv has had a seat at the negotiating table. Russias deputy foreign minister reported that if the U.S. military activity continues to provoke and pressure Russia, Moscow may potentially deploy its troops to Venezuela and Cuba in retaliation.

Russian-led troops have begun to withdraw from Kazakhstan following violent protests and political unrest in the nations capital, writes Reuters. Upon their departure, Kazak President Kassym-Jomart Tokayev thanked the Russian-led troops for their assistance with stabilizing the country. Vladimir Putin declared Russias mission in Kazakhstan to be a success and expressed hope that this practice of using our armed forces will be studied further. The Russian defense minister reported that the withdrawal of Russian-led troops will be completed by Jan. 19.

In the first criminal trial concerning state-led torture in Syria, a German court has convicted a Syrian colonel for crimes against humanity, according to BBC News. Anwar Raslan was sentenced to life in prison for his involvement in the torture of more than 4,000 incarcerated people at a Syrian prison. UN rights chief Michelle Bachelet said that Raslans conviction was a landmark leap forward in the pursuit of truth and justice. This ruling marks the first time a criminal court has recognized that the Assad regime has committed crimes against humanity against Syrian citizens.

ICYMI: Yesterday on Lawfare

Jen Patja Howell shared an episode of the Lawfare Podcast in which Natalie Orpett, Benjamin Wittes and Alan Rozenshtein discuss Trump v. Thompson, presidential immunity and the First Amendment.

Paul Rosenzweig analyzed the problems with the U.S. approach to homeland security.

Hadley Baker shared an episode of Lawfare No Bull that provides a straightforward account of Tuesdays Senate Judiciary Committee hearing on the threat of domestic terrorism

Howell also shared an episode of Rational Security in which Alan Rozenshtein, Quinta Jurecic and Scott R. Anderson sat down with Natalie Orpett to discuss the weeks biggest national security news stories.

Jordan Schneider shared an episode of ChinaTalk that features a discussion with Stony Brook Universitys Michael Barnhart about what U.S.-China relations can learn from U.S.-Japan relations in the leadup to WWII.

Elena Kagan and Alan Rozenshtein analyzed the five-hour Thompson v. Trump oral argument that took place on Jan. 10.

Email the Roundup Team noteworthy law and security-related articles to include, and follow us on Twitter and Facebook for additional commentary on these issues. Sign up to receive Lawfare in your inbox. Check out relevant job openings on our Job Board.

Read the original here:

Today's Headlines and Commentary - Lawfare - Lawfare

After Oral Argument, the Future of Thompson v. Trump Remains Unclear – Lawfare

Hours into a marathon oral argument on Jan. 10, Judge Amit Mehta of the U.S. District Court for the District of Columbia observed that if there is one thing this hearing has shown it's that this is not an easy case. For nearly five hours Judge Mehta heard arguments about whether former President Donald Trump, Rep. Mo Brooks, Rudy Giuliani and others could be held civilly liable for their role in the Jan. 6 Capitol insurrection. The main lawsuits, brought by Reps. Bennie Thompson and Eric Swalwell, allege violations of 42 U.S.C. 1985(1), a Reconstruction-era statute that created civil liability for conspiracies to prevent public officials from holding any office or discharging any duties.

Addressing issues common to the three lawsuits, Judge Mehta wrestled with formidable defenses raised by Trump and his co-defendants: chiefly that Trump and Books are immune from civil liability for actions taken as part of their official duties, and that the defendants statements leading up to the siege of the Capitol could not satisfy the elements of conspiracy, especially to the extent that the statements were protected speech under the First Amendment.

Immunity

Trump lawyer Jesse Binnall argued for an expansive, highly formalistic vision of presidential immunity, relying on the Supreme Courts ruling in Nixon v. Fitzgerald that presidents are entitled to absolute immunity from civil liability for actions while in office that fall within the outer perimeter of their official responsibility. The crux of Binnalls argument was that the court must entirely ignore the content of Trumps speech on Jan. 6his remarks at the Ellipse and on Twitter over the course of the dayand look only at the presidents conduct to analyze whether he was acting in his official capacity. Because Trump was addressing the American people, Binnall argued, he was acting within his official duties as president and must enjoy immunity, especially since the subject of his speech, electoral integrity, is a matter of immense public concern.

Judge Mehta was skeptical of this all-encompassing vision of the presidents official duties, which potentially would make the president immune from civil liability anytime he opens his mouth. Judge Mehta pushed Binnall on whether there is anything that a president could do or say while in officefor example, as part of a campaignthat would not be immune from liability under his expansive theory of presidential immunity. Binnall said that he could not name an example of anything the president could say that would not fall within his official duties under this theory, but that perhaps signing a lease on a campaign office would not fall within his responsibilities as president.

But if Binnall failed to give Judge Mehta a reasonable standard for the scope of presidential immunity, the plaintiffs struggled to offer one that would withhold immunity in this case while nevertheless being consistent with precedent. The plaintiffs argued that Trump should not enjoy immunity because fomenting an insurrection against Congress was clearly unconstitutional and thus outside his official duties. But as Judge Mehta noted, Fitzgerald held that presidential immunity did not depend on the legality of the presidents action. The plaintiffs were left to argue that Trumps conduct surrounding Jan. 6 was so outrageous that it was clearly beyond the scope of his presidential responsibilities. But where exactly to draw that line remained unclear.

The question of the scope of official duties was also at the center of Brooks claim that he should be immune from liability under the Westfall Act, which requires the government to act as the defendant when federal employees are sued for tort liability for official actions. The Department of Justice joined the plaintiffs in arguing that Brookswho spoke before Trump on Jan. 6 and declared that Todays the day American patriots start taking down names and kicking asswas campaigning and therefore acting outside the scope of his official duties; as a result, Brooks should not be immune from civil liability. But Brooks, who argued on his own behalf, emphasized that his motivation in speaking at the Jan. 6 rally was not simply to support Republican candidates in future elections, but also to convince his fellow congresspeople to vote against the certification of the electoral college votes.

First Amendment

The other major hurdle for the plaintiffs is the First Amendment, which generally protects the sort of political speech that Trump, Brooks and the other rally speakers engaged in (and to that extent cannot serve as the predicate for the plaintiffs conspiracy charges). The plaintiffs emphasized that the defendants could be held liable under even the highly speech-protective standard of Brandenburg v. Ohio, which permits liability for advocacy of the use of force or of law except where such advocacy is directed to inciting or producing imminent lawless action and is likely to incite or produce such action.

In a heated exchange, Binnall, Trumps lawyer, repeatedly pointed to several inflammatory statements that the Democratic plaintiffs had themselves previously made, arguing that if Trumps language was found to be incitement to violence here, but similar language used by others elsewhere was not, the court would be failing to apply the First Amendment equally to Democrats and Republicans. Judge Mehta sharply rebuked Binnall for engaging in whataboutism and for suggesting that he was judging Trump and his co-defendants speech more harshly because of their party.

Like the discussion of presidential immunity, the First Amendment portion of the argument was inconclusive. On the one hand, Trumps words did not explicitly call for violence and were on their face far less inflammatory than what the Supreme Court upheld in Brandenburg and in many cases since. On the other hand, the broader context of Trumps speech, from his months-long campaign to discredit the election to his failure to act after the attack on the Capitol began, suggests, as Mehta noted, that Trumps speech went beyond ordinary political rhetoric, even if it was not the sort of speech that typically qualifies as conspiracy to commit violence.

Ultimately, and despite hours of questioning, Judge Mehta did not tip his hand as to how he will rule on the many complex legal issues that the lawsuits raise. But given the high political and legal stakes, its unlikely that Judge Mehtas decision will be the last word. The parties will almost certainly appeal any outcome to the U.S. Court of Appeals for the D.C. Circuit, and this case may well end up before the Supreme Court, especially on the central issue of presidential immunity.

Read the original:

After Oral Argument, the Future of Thompson v. Trump Remains Unclear - Lawfare

Proving Trump conspired to incite the Jan. 6 insurrection is a tall order – Chicago Sun-Times

After Donald Trumps second impeachment trial ended in acquittal, Senate Minority Leader Mitch McConnell (R-Kentucky) suggested that the former president could still be held civilly or criminally liable for his role in the Capitol riot that happened a year ago last Thursday. But as three lawsuits that a federal judge considered this week show, those options require proving that Trump deliberately provoked the violence that day, which is a tall order.

The Trump supporters who broke into the Capitol, interrupting the congressional tally of the presidential election results, came to Washington, D.C., at his behest. They were motivated by Trumps fantasy of a stolen election, which he had been promoting for months and reiterated in a fiery pre-riot speech at a rally a mile and a half from the Capitol.

I know that everyone here will soon be marching over to the Capitol building to peacefully and patriotically make your voices heard today, Trump said. While he did not advocate violence, it was foreseeable that at least some of his supporters would interpret his exhortation to fight like hell in defense of a supposedly imperiled democracy as a justification for the use of force.

Still, there is a big difference between reckless rhetoric, which is protected by the First Amendment, and the criminal conspiracy described in lawsuits filed by Rep. Eric Swalwell (D-California), other House Democrats and two Capitol Police officers. All three complaints allege that Trump violated the Ku Klux Klan Act of 1871 by conspiring to use threats, force and intimidation to stop government officials from carrying out their duties.

To prove that claim, the plaintiffs must do more than show that Trump ginned up his supporters outrage with false election fraud claims, or even that he did so in circumstances where he should have known violence was likely. They have to show that the Capitol riot was the culmination of a plan to violently disrupt the ratification of Joe Bidens victory, a scheme in which Trump himself intentionally participated.

Capitol Police officers James Blassingame and Sidney Hemby also claim that Trump violated a provision of the D.C. Code that makes it a criminal offense to willfully incite or urge other persons to engage in a riot. In addition to the requirement that the offense be committed willfully, prosecution for incitement is constrained by the First Amendment.

Even advocacy of illegal behavior, the Supreme Court ruled in the 1969 case Brandenburg v. Ohio, is constitutionally protected unless it is not only likely to incite imminent lawless action but also directed at doing so. Another exception to the First Amendment, for true threats, involves statements where the speaker means to communicate a serious expression of an intent to commit an act of unlawful violence to a particular individual or group of individuals.

In a brief supporting Swalwells lawsuit, three law professors, joined by legendary First Amendment attorney Floyd Abrams, say these exceptions likely apply here. But whether that is so hinges on what we surmise Trump was thinking when he gave his speech.

Trump explicitly urged Mike Pence, for instance, to reject electoral votes for Biden, a power the vice president did not actually have. But Trump did not threaten Pence with an act of unlawful violence, and inferring such a threat requires speculation about what Trump meant to communicate in light of what his supporters did afterward.

It is likewise not at all obvious that Trump wanted to cause a riot, an outcome that failed to accomplish his ostensible goal, led to his second impeachment and provoked harsh criticism from Republican legislators such as McConnell. If this was all part of a plan, it was a pretty stupid plan.

The urge to punish Trump for his reckless rhetoric is understandable but dangerous. If his opponents succeed, they may regret establishing a precedent that speakers who neither practice nor preach violence can be held legally liable for the conduct of listeners inspired by their words.

Jacob Sullum is a senior editor at Reason magazine. Follow him on Twitter: @JacobSullum.

See original here:

Proving Trump conspired to incite the Jan. 6 insurrection is a tall order - Chicago Sun-Times

Researchers Use Machine Learning to Model Proteins Linked to Cancer – Livermore Independent

Lawrence Livermore National Laboratory (LLNL) researchers and a multi-institutional team of scientists have developed a machine learning-backed model showing the importance of lipids to the signaling dynamics of RAS, a family of proteins whose mutations are linked to numerous cancers.

Lipids are fatty acid organic compounds that are insoluble in water, but soluble in organic solvents.

In a paper published in the Proceedings of the National Academy of Sciences, researchers detail the methodology behind the Multiscale Machine-Learned Modeling Infrastructure (MuMMI), which simulates the behavior of RAS proteins on a cell membrane, their interactions with lipids which help make up cell membranes and the activation of RAS signaling on a macro and molecular level.

According to the researchers, the data indicates that lipids rather than protein interfaces govern both RAS orientation and the accumulation of RAS proteins.

We always knew lipids were important, said LLNL computer scientist and lead author Helgi Ingolfsson. You need some of them, otherwise you dont have this behavior. But after that, scientists didnt know what was important about them.

Normally, RAS proteins receive and follow signals to switch between active and inactive states, but as the proteins move along the cell membrane they combine with other proteins and can activate signaling behavior.

Mutated RAS proteins can become stuck in an uncontrollable, always on growth state, which is seen in the formation of about 30% of all cancers, particularly pancreatic, lung and colorectal cancers.

The research is showing us that lipids are a key player, Ingolfsson said. By modulating the lipids and different lipid environments, RAS changes its orientation, and you can actually change the signaling (between grow and not grow) by changing the lipids underneath.

Researchers said the MuMMI framework represents a fundamentally new technology in computational biology and could be used to improve their basic understanding of RAS protein binding.

The research is part of a pilot project of the Joint Design of Advanced Computing Solutions for Cancer, a collaboration between the Department of Energy, National Cancer Institute, and other organizations.

Traditional researchers can simulate only a small, fixed number of proteins and one lipid composition at a time, Ingolfsson explained, and they need to know which lipids are important to model beforehand. With the MuMMI framework, researchers can simulate thousands of different cell compositions derived from the macro model, allowing them to answer questions about RAS-lipid interactions that previously would be possible only with a multiscale simulation.

Were demonstrating that the old way of doing things is starting to be outdated, Ingolfsson said. At Livermore, we have enormous computing power, we have a lot of people working on this and we can show what can be possible.

Go here to read the rest:
Researchers Use Machine Learning to Model Proteins Linked to Cancer - Livermore Independent

Physics and the machine-learning black box | MIT News | Massachusetts Institute of Technology – MIT News

Machine-learning algorithms are often referred to as a black box. Once data are put into an algorithm, its not always known exactly how the algorithm arrives at its prediction. This can be particularly frustrating when things go wrong. A new mechanical engineering (MechE) course at MIT teaches students how to tackle the black box problem, through a combination of data science and physics-based engineering.

In class 2.C01 (Physical Systems Modeling and Design Using Machine Learning), Professor George Barbastathis demonstrates how mechanical engineers can use their unique knowledge of physical systems to keep algorithms in check and develop more accurate predictions.

I wanted to take 2.C01 because machine-learning models are usually a black box, but this class taught us how to construct a system model that is informed by physics so we can peek inside, explains Crystal Owens, a mechanical engineering graduate student who took the course in spring 2021.

As chair of the Committee on the Strategic Integration of Data Science into Mechanical Engineering, Barbastathis has had many conversations with mechanical engineering students, researchers, and faculty to better understand the challenges and successes theyve had using machine learning in their work.

One comment we heard frequently was that these colleagues can see the value of data science methods for problems they are facing in their mechanical engineering-centric research; yet they are lacking the tools to make the most out of it, says Barbastathis. Mechanical, civil, electrical, and other types of engineers want a fundamental understanding of data principles without having to convert themselves to being full-time data scientists or AI researchers.

Additionally, as mechanical engineering students move on from MIT to their careers, many will need to manage data scientists on their teams someday. Barbastathis hopes to set these students up for success with class 2.C01.

Bridging MechE and the MIT Schwarzman College of Computing

Class 2.C01 is part of the MIT Schwarzman College of Computings Common Ground for Computing Education. The goal of these classes is to connect computer science and artificial intelligence with other disciplines, for example, connecting data science with physics-based disciplines like mechanical engineering. Students take the course alongside 6.C01 (Modeling with Machine Learning: from Algorithms to Applications), taught by professors of electrical engineering and computer science Regina Barzilay and Tommi Jaakkola.

The two classes are taught concurrently during the semester, exposing students to both fundamentals in machine learning and domain-specific applications in mechanical engineering.

In 2.C01, Barbastathis highlights how complementary physics-based engineering and data science are. Physical laws present a number of ambiguities and unknowns, ranging from temperature and humidity to electromagnetic forces. Data science can be used to predict these physical phenomena. Meanwhile, having an understanding of physical systems helps ensure the resulting output of an algorithm is accurate and explainable.

Whats needed is a deeper combined understanding of the associated physical phenomena and the principles of data science, machine learning in particular, to close the gap, adds Barbastathis. By combining data with physical principles, the new revolution in physics-based engineering is relatively immune to the black box problem facing other types of machine learning.

Equipped with a working knowledge of machine-learning topics covered in class 6.C402 and a deeper understanding of how to pair data science with physics, students are charged with developing a final project that solves for an actual physical system.

Developing solutions for real-world physical systems

For their final project, students in 2.C01 are asked to identify a real-world problem that requires data science to address the ambiguity inherent in physical systems. After obtaining all relevant data, students are asked to select a machine-learning method, implement their chosen solution, and present and critique the results.

Topics this past semester ranged from weather forecasting to the flow of gas in combustion engines, with two student teams drawing inspiration from the ongoing Covid-19 pandemic.

Owens and her teammates, fellow graduate students Arun Krishnadas and Joshua David John Rathinaraj, set out to develop a model for the Covid-19 vaccine rollout.

We developed a method of combining a neural network with a susceptible-infected-recovered (SIR) epidemiological model to create a physics-informed prediction system for the spread of Covid-19 after vaccinations started, explains Owens.

The team accounted for various unknowns including population mobility, weather, and political climate. This combined approach resulted in a prediction of Covid-19s spread during the vaccine rollout that was more reliable than using either the SIR model or a neural network alone.

Another team, including graduate student Yiwen Hu, developed a model to predict mutation rates in Covid-19, a topic that became all too pertinent as the delta variant began its global spread.

We used machine learning to predict the time-series-based mutation rate of Covid-19, and then incorporated that as an independent parameter into the prediction of pandemic dynamics to see if it could help us better predict the trend of the Covid-19 pandemic, says Hu.

Hu, who had previously conducted research into how vibrations on coronavirus protein spikes affect infection rates, hopes to apply the physics-based machine-learning approaches she learned in 2.C01 to her research on de novo protein design.

Whatever the physical system students addressed in their final projects, Barbastathis was careful to stress one unifying goal: the need to assess ethical implications in data science. While more traditional computing methods like face or voice recognition have proven to be rife with ethical issues, there is an opportunity to combine physical systems with machine learning in a fair, ethical way.

We must ensure that collection and use of data are carried out equitably and inclusively, respecting the diversity in our society and avoiding well-known problems that computer scientists in the past have run into, says Barbastathis.

Barbastathis hopes that by encouraging mechanical engineering students to be both ethics-literate and well-versed in data science, they can move on to develop reliable, ethically sound solutions and predictions for physical-based engineering challenges.

Read more:
Physics and the machine-learning black box | MIT News | Massachusetts Institute of Technology - MIT News

68% of CTOs have Implemented Machine Learning at their Organization – insideBIGDATA

55% of businesses now employ at least one team member dedicated to AI/ML solutions, although only 15% have their own separate AI division

Research fromSTX Next, Europes largest software development company specializing in the Python programming language, has found that 68% of chief technical officers (CTOs) have implemented machine learning at their company. This makes it overwhelmingly the most popular subset of AI, with others such as natural language processing (NLP), pattern recognition and deep learning also showing considerable growth.

Despite the popularity of AI and its various subsets, its also clear that AI implementation is still in its early phases and theres progress to be made in recruiting the talent needed for its development. In fact, 63% of CTOs reported that they arent actively hiring AI talent and of those that are, over 50% report facing recruitment challenges.

The findings were taken from STX Nexts 2021 Global CTO Survey, which gathered insights from 500 global CTOs about their organizations tech stack and what theyre looking to add to it in the future. Other key findings from the research included:

ukasz Grzybowski, Head of Machine Learning & Data Engineering at STX Next, said: The implementation of AI and its subsets in many companies is still in its early stages, as evidenced by the prevalence of small AI teams.

Its unsurprising to see machine learning as a definite leader when it comes to future technologies as its applications are becoming more widespread every day. Whats less obvious is the skills that people will need to take full advantage of its growth and face the challenges that will arise alongside it. Its important that CTOs and other leaders are wise to these challenges, and are willing to take the steps to increase their AI expertise in order to maintain their innovative edge.

Deep learning is a good example of where there is plenty of room for progress to be made. It is one of the fastest developing areas of AI, in particular when it comes to its application in natural language processing, natural language understanding, chatbots, and computer vision. Many innovative companies are trying to use deep learning to process unstructured data such as images, sounds, and text.

However, AI is still most commonly used to process structured data, which is evidenced by the high popularity of classical machine learning methods such as linear or logistic regression and decision trees.

Grzybowski concluded: To adapt AI to unstructured data, the technology will need to mature further. This is why initiatives such as MLOps have a major role to play, as long-term success will only be achieved when data scientists and operations professionals are all on the same page and fully committed to making AI and machine learning work for everyone.

Sign up for the free insideBIGDATAnewsletter.

Join us on Twitter:@InsideBigData1 https://twitter.com/InsideBigData1

Read more:
68% of CTOs have Implemented Machine Learning at their Organization - insideBIGDATA

Advanced data science, machine learning and the power of knowledge graphs: What can we expect from this combination? – IDG Connect

This is contributed article by Maya Natarajan, Sr. Director Product Marketing, Neo4j.

From bridging data silos and building data fabrics to accelerating machine learning (ML) and artificial intelligence (AI) adoption, knowledge graphs are foundational and allow businesses to go beyond digital transformation. Defined by The Turing Institute, the UK's national institute for data science and AI, as the best way to encode knowledge to use at scale in open, evolving, decentralised systems, knowledge graphs are a perfect foundation for advanced data science initiatives. So why arent they better known and exploited?

This is a problem. Business leaders know the value of their data and are keenly aware that it holds the answers to their most pressing business questions. The insights to improve decision-making and enhance business performance they need, however, arent easy to elicit. Hence the widespread interest in machine learning.

Knowledge graphs can help an organisation trying to get machine learning to a useful production status and out of the lab. Thats because knowledge graphs are a special, non-disruptive insight layer on top of this complex data resource. They drive intelligence into data to significantly enhance its value, but without changing any of the existing data infrastructure. Lets look at how.

Knowledge graphs make existing technologies better by providing better data management, better predictions, and better innovation, in part because they fuel AI and machine learning. In practice, knowledge graph use cases divide into two groupings: actioning and decisioning. The actioning graphs aim is to drive action by providing assurance or insight. Data actioning graphs automate processes for better outcomes by providing data assurance, discovery, and insight, and include examples like data lineage, data provenance, data governance, compliance, and risk management.

A great example of a data actioning graph is a knowledge graph that tracks objects in space, both functional equipment and broken equipment. The ASTRIAGraph project monitors the Earths orbit for space objects, including functioning hardware and other space junk, striving for safety, security, and sustainability. Using a knowledge graph, the team can categorise a lot of disparate space domain data to locate and track objects from the size of a mobile phone to the largest satellite. The ASTRIAGraph predicts their trajectory, minimises risk, and provides complete visibility.With the goal of maximising decision intelligence, ASTRIAGraph curates information and creates models of the space domain and environment.

The real magic of knowledge graphs comes into play as you use them to support AI and machine learning, uncovering patterns and anomalies. A decisioning knowledge graph surfaces data trends to augment analytics, machine learning, and data science initiatives. With all of this, its not surprising that Gartner recently stated, Up to 50% of Gartner inquiries on the topic of AI involve discussion of the use of graph technology.

We know this from speaking to customers. Moving from an actioning graph to sophisticated decisioning graphs fuelling AI and machine learning is a typical graph technology journey for many data science teams we work with, with knowledge graphs at the centre.

From data sourcing to training machine learning models to analysing predictions and applying results, knowledge graphs enhance every step of the machine learning process.

In the initial step of data sourcing, knowledge graphs can be used for data lineage to track data that feeds machine learning. In the next phase of training a machine learning model, knowledge graphs allow for graph feature engineering using simple graph queries or more complex graph algorithms, like centrality, community detection, and the like. The results of such algorithms can be written back to the knowledge graph, further enriching it.

The next step forward in sophistication is the use of graph embeddings. Graph embeddings offer a way of encoding the nodes and the relationships in a knowledge graph into a structure that's suitable for machine learning. Effectively, embeddings turn your knowledge graph into numbers and learn all its features. Relationships are highly predictive of behaviour, so using connected, contextualised features maximises the predictive power of machine learning models.

Once a machine learning model has been developed, knowledge graphs can be used for investigations and counterfactual analyses by data scientists to understand if a model is useful and making accurate predictions.

Lets look at decisioning in action. UBS, for example, built a detailed data lineage and governance tool that offers deep transparency into the data flows that feed its risk reporting mechanisms to meet finance compliance regulations.

Another example is NASA, which has decades of mission experience that wasnt well catalogued. NASA built a knowledge graph-enhanced application to comb through millions of documents, reports, project data, lessons learned, scientific research, medical analysis, geospatial data, and IT logs. As a result, an old breakthrough from the Apollo era in the 1960s solved a problematic issue in its 21st Century Orion class of crewed spaceships. It saved a million dollars of taxpayer money by heading off the need for two years of work reinventing the wheel.

And in the life sciences, one large global pharmaceutical company is working with knowledge graphs to help clinicians know when to best intervene for complex diseases. Its data science team used graph algorithms to find patients that had specific journey types and patterns, and find others with similar experiences. This insight is used to train its machine learning model, analyse predictions, and bring back results to help clinicians make better decisions. And were talking about scalethis companys knowledge graph holds three years of visits, tests, and patient diagnoses across tens of billions of records.

By using the power of knowledge graphs, AI and machine learning models are better able to represent relationships. That means the organisations using them can find more accurate interpretations of complex data, putting context back into data, and training AI to be a trustworthy partner.

Its a powerful trend that we see more and more in data science. No wonder the c-suite is waking up to this innovation.

Maya Natarajan is Sr. Director Product Marketing at native graph database leader Neo4j

Read more from the original source:
Advanced data science, machine learning and the power of knowledge graphs: What can we expect from this combination? - IDG Connect

Machine Learning and 5G Are Crucial to Scale the Metaverse – BBN Times

Machine learning and 5G can attract more people in the metaverse, blurring the lines between the virtual and real worlds.

The concept of metaverse is closely related to advanced technologies such as artificial intelligence (AI), machine learning (ML), augmented reality (AR), virtual reality (VR), blockchain, 5G and the internet of things (IoT).

Improvedtechnology will allow avatars to use body language effectively and better convey human emotions producinga feeling of real communication in a virtual space.

ARand VR won't be the only critical components of themetaverse, 5G and machine learning are also crucial.

Source: Jon Radoff

The metaverse is a future iteration of the internet, made up of 3D virtual spaces linked into a perceived virtual universe. In a broader sense, it may not only refer to virtual worlds but the entire spectrum of augmented and virtual reality.

Image Credit: Unit 2 Games Limited

Users can interact with 3D digital objects and 3D virtual avatars of each other in a complex manner that mimics the real world.

The idea of the metaverse was first coined by science fiction writer Neal Stephenson in the early 90s and was eventually developed in parts by companies like Second Life, Decentraland, Microsoft, and most recently Meta.

In this virtual world,people can interact, hold meetings, buy property and do even more.

Themetaverseconcept relies on augmented and virtual reality (AR/VR) in combination with machine learning, 5G, the internet of things (IoT) and blockchain to create a scalable digital world.

Machine Learning is defined as the field of AI that applies statistical methods to enable computer systems to learn from the data towards an end goal.

The types of machine learning include supervised, unsupervised, semi-supervised and reinforcementlearning.

Supervised Learning: a learning algorithm that works with data that is labelled (annotated). Supervised Learning Algorithms may use Classification or Numeric Prediction. Classification (Logistic Regression, Decision Tree, KNN, Random Forest, SVM, Naive Bayes, etc), is the process of predicting the class of given data points.for example learning to classify fruits with labelled images of fruits as apple, orange, lemon, etc. Regression algorithms (Linear Regression, KNN, Gradient Boosting & AdaBoost, etc) are used for the prediction of continuous numerical values.

Unsupervised Learning is a learning algorithm to discover patterns hidden in data that is not labelled (annotated). An example is segmenting customers into different clusters. Examples include clustering with K-Means, and pattern discovery. A powerful technique from Deep Learning, known as Generative Adversarial Networks (GANs), uses unsupervised learning.

Semi-Supervised Learning: is a learning algorithm only when a small fraction of the data is labelled. An example is provided byDataRobot"When you dont have enough labelled data to produce an accurate model and you dont have the ability or resources to get more, you can use semi-supervised techniques to increase the size of your training data. For example, imagine you are developing a model for a large bank intended to detect fraud. Some fraud you know about, but other instances of fraud slipped by without your knowledge. You can label the dataset with the fraud instances youre aware of, but the rest of your data will remain unlabelled. "

Reinforcement Learning entails Q-Learning and involves an agent taking appropriate actions in order to maximize a reward in a particular situation. It is used by an intelligent agent to solve for the optimal behaviour or path that the agent should take in a specific situation.

Machine Learning plays a major role in everyday applications via facial recognition, voice search, natural language processing (NLP), faster computing, and all sorts of other under-the-hood processes.It hasthe potential to parse huge volumes of data at lightning speed to generate insights and drive action, which can significantly improve the interaction of users in the metaverse.

Source: IT World Canada

5G is thefifth generation wireless technology. It can provide higher speed, lower latency and greater capacity than 4G LTE networks.

The impact of 5G on the metaverse is clearly the increased number of devices that can be connected to the network. All connected devices are able to communicate with each other in real-time and exchange information.

5G is up to 20 times faster than 4G, it offers more than just faster speeds. Due to its low latency, 5G speeds will allow developers to create applications that take full advantage of improved response times, including near real-time video transmission for sporting events or security purposes.

The combination of 5G and machine learning is truly transformative. Replacing traditional wireless algorithms with advanced machine learningalgorithms will dramatically reduce power consumption and improve the performance of 5G networks which support a metaverse environment.

A key piece of the metaverse puzzle is that organizations need advanced data to create specific electronics equipment that will help everyone connect to the metaverse. At the moment, VR headsets or AR glasses are still experimental products at best. Machine learning can help organisations build modern VR and AR devices, which will keep on improving.

Source: Qualcomm

Machine learning and 5G could make the metaverse viral, as they are already two of the most disruptive technologies the world has seen in decades.

In order to take the concept of metaverse to another level, internet connection has to improve around the world.While 5G networks are set to roll out in many countries, a faster internet network is needed to connect seamlessly. Machine learning is also in its infancy as well.

A cultural shift in the tech world has to occur to attract more users. The covid-19 pandemic also plays a major role as people are not ready for a disruptive digital world, patience is key here. Better virtual and augmented reality devices are needed.

The concept of the metaverse could fail if it is rushed and that companies and users aren't prepared for the next version of the internet.

The hardest technology challenge of our time may be fitting a supercomputer into the frame of normal-looking glasses.

Mark Zuckerberg

Read more:
Machine Learning and 5G Are Crucial to Scale the Metaverse - BBN Times