Judge rejects request by Boston cops to dismiss First Amendment action over the way they pepper sprayed and hit George Floyd protesters in 2020 -…

A federal judge ruled today that four people at a George Floyd vigil on the Common on May 31, 2020 can try to convince a jury that Boston Police officers violated their First Amendment rights by attacking them with pepper spray, fists and a bicycle afterwards and that the city created a culture where such a thing could happen.

Among other reasons to seek dismissal, the cops alleged they did not violate the protesters' First Amendment rights because they did not know the four were on Tremont Street because of the Common protest and so did not know they had a First Amendment right to be there.

That assertion "strains credulity," US District Court Judge Alison Burroughs wrote in a decision today that rejected requests by the cops and the city to reject the First Amendment and civil-rights allegations by the four protesters for what happened after police broke up the vigil and ordered nearby T stops shut, on a night that ended with violence and looting across downtown, the Back Bay and the South End.

Here, the chronology of events, the location of each incident, and all other surrounding circumstances, plainly allow for a reasonable inference that each of the Officer Defendants would have known the Plaintiffs were protestors and that they used force against them for that reason. ... Nothing in the record thus far, which includes photos of the Plaintiffs with their arms up and backing away from officers, provides a plausible non-retaliatory motive for the Officer Defendants use of physical force against the Plaintiffs. Further, because the uses of force against Ackers, Hall, and Chambers-Maher occurred while the officers were being openly recorded, it would be reasonable to infer that the civilians filming of the officers formed an unlawful retaliatory motive for the use of force. ... Put simply, the Officer Defendants argument that they could not have known that the Plaintiffs participated in the protest is untenable. Based on the record currently before the Court, it is evident that each one of these incidents occurred while the BPD was seeking to disperse protesters.

Burroughs added, however, that the officers will be able to better rebut the allegation than they have to date during pre-trial discovery and then at trial;

The point of discovery and then trial will be to sort out whether these particular uses of force did or did not implicate the First Amendment.

But, she continued:

Courts around the country, flooded with First Amendments claims pleaded on similar facts following the May 2020 protests, have agreed that the use of force against non-violent protestors can support the inference that officers meant to intimidate protestors and deter antipolice messaging.

Burroughs also allowed the four to continue their lawsuit against the city itself for allegedly creating an atmosphere that allowed and even encouraged misbehavior by police, in large part by ignoring complaints against officers in the past, but also through "a custom of using excessive force." But as she did with the police on the First Amendment issue, she cautioned the four protesters haven't really made a good, detailed case of this to date - something they will have to do at trial to win against the city.

To be sure, Plaintiffs support for this claim is presently thin, particularly since Plaintiffs have done little to link their allegations together to present a systemic pattern of persistent failure to discipline or investigate, but more is not required at the pleading stage. Plaintiffs have specifically articulated that the City knew constitutional violations occurred and either chose not to investigate or otherwise delayed or discouraged investigation. Taking Plaintiffs factual allegations as true and viewing the Amended Complaint in the light most favorable to Plaintiffs, the allegations allow for a reasonable inference that the City has a custom of failing to discipline police misconduct.

Burroughs continued:

The Amended Complaint contains numerous allegations that officers used OC spray, batons, and other physical force against the four Plaintiffs during the May 31 protest. Plaintiffs sufficiently allege, though just barely, that similar constitutional violations occurred on May 29, giving decisionmakers sufficient notice that officers would continue to use unreasonable force against peaceful protestors in the demonstrations to come. The City's argument that the allegations are not enough to support a Monell claim because they rest only on "one night of civil unrest" is unavailing. In addition to the fact that Plaintiffs have suggested that similar conduct occurred during demonstrations on surrounding days, "egregious instances of misconduct" even when "relatively few in number but following a common design, may support an inference that the instances would not occur but for municipal tolerance of the practice in question." Foley v. City of Lowell, 948 F.2d 10, 14 (1st Cir. 1991). ... Here, Plaintiffs describe four similar incidents of excessive force used against peaceful protesters. Further, Plaintiffs may not know, or cannot know, without discovery the full extent of the unreasonable force used by the City against protesters during the May 2020 protests. This Court, in line with several other district courts presented with similar facts, finds that Plaintiffs have sufficiently pleaded that the City had notice of the unlawful use of force against protestors and was deliberately indifferent to those constitutional violations.

She also pointed to a decision by Police Commissioner William Gross to have riot batons distributed to officers beforehand and to have nearby T stations shut as the vigil was dispersed as legitimate acts for a jury to consider whether BPD had a policy that led to the incidents:

Because three of the four Plaintiffs injuries occurred while they were trying to leave the protest area and some of the alleged injuries were caused by blows from riot batons, it can be reasonably inferred that Commissioner Grosss policy decisions led to the constitutional deprivations. ...

Plaintiffs will have to overcome significant issues of proof if they are to prevail at trial. Nonetheless, the Court finds that, at this stage, Plaintiffs have adequately pleaded municipal liability based on the role that City customs and policies allegedly played in the constitutional violations.

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Judge rejects request by Boston cops to dismiss First Amendment action over the way they pepper sprayed and hit George Floyd protesters in 2020 -...

Sports Illustrated Is Now A Bullhorn For Attacks On The First Amendment – The Federalist

Younger readers probably wont comprehend how important magazines like Sports Illustrated were in pre-internet culture. Most sports news wasfound in local papers and in short segments at 10 p.m. on the nightly news. Sports Illustrated was oftenthough, periodically, competition would pop upthe sole venue in which a sports fan could find deeply reported, well-crafted features and profiles, not to mention often-remarkable photography (the swimsuit issues, naturally, sold best). The magazines circulation hit around 3.5 million in the mid-1980s, with another million copies being bought on newsstands.

In my late 20s, I brieflyworkedfor the company (well, the website, which was then called CNN/SI.comperhaps a portend of terrible things to come), where I occasionally interacted with one of my writing heroes, Frank Deford. What a dream it was. I would have done it for free. I guess I almost did.

Ive largely ignored the magazine for the past decade or so, not for any philosophical reasons or any animosity, but with all the choices it simply fell off my radar. But after running across an astoundingly nonsensical pieceheadlinedWhen Faith and Football Teamed Up Against American Democracy, Im glad I did.

Ostensibly, the feature is about Kennedy v. Bremerton School District, a SCOTUS case regarding a school district punishing a football coach named Joseph Kennedy for a 30-second silent prayer on the 50-yard line after every game. The pieces subhead describes the case as so:

The U.S. Supreme Court will soon decide the case of a football coach at a public high school who was told he wasnt allowed to pray on the field in front of players. The expected result is a win for the coachand the further erosion of the separation between church and state.

In frontof players? Can you imagine? How will our brittle democracy survive an open display of religiosity? Greg Bishop, who could easily have written this piece for The Nation, offers no explanation of how a prayer is eroding separation of Church and State. Even this atheist, after all, understands that the Establishment Clause doesnt ban praying in public placesnot in schools, and not even in Congress, where prayers are recited before every session.

Bishop anoints Rachel Laser of Americans United for Separation of Church and State his proxy, allowing her to frame the debate over Kennedy in the most preposterously hyperbolic, partisan terms imaginable, even though the only thing her organization excels at is losing cases. The bad-faith retelling of Kennedys story is crammed with partisan platitudes about democracy being under attack on issues like voting rights, LGBTQ rights, and the potential overturning of Roe v. Wade.

Now, its unimaginable that a major publication would allow areporter to throw around phrases like voting integrity, religious freedom, and protecting the life of the unbornwithout quotation marks intimating that the ideas arent realand thats probably always been the case. Though the piece brings upRoethree times, no one explains how a court (concerned solely with the constitutionality of laws) is undermining democratic institutions by giving abortion rights, unmentioned in the Constitution, back to voters. Washington State, home of Bremerton High School, sadly, will not be restricting abortion any time soon.

In any event, Bishop also uses appeals to authority, tapping independent scholars or legal experts who hold no vested interest in the outcomeone of the only names offered isconspiracy theoristLaurence Tribe. He warns readers about the nefarious, big-money forces propping up Kennedy. First Liberty($7,255,961in assets), writes Bishop, is a powerful Christian conservative law firm, part of apowerful right-wing machinepowerfulis the key word herewhile Americans United for Separation of Church and State($11,141,577in assets, not counting in-kind contributions from places like the Meredith Corporation, which has $6.727 billion in assets), are simply terrified and transported to an alternate universe of disinformation and propagandaand, in that world, even democracy is in danger.

Disinformation? Its all just progressive mad libs. Thats what happens when democracy is a euphemism for achieving political ends in whatever fashion happens to be convenient. Sometimes, when the numbers are there, it means crass majoritarianism and centralized federal power; and when the numbers arent there, it can mean compulsion or a court dictating rights by fiat.

In this case, a school district, not the coach, is attempting to limit speech. There is no prohibition on praying in public institutions. Such a prohibition has never existed. Any scholarand Bishop claims to have spoken to many for the piecewho claims that the Constitutions authors would have found the act of kneeling after a competition perilous to foundational American ideals is a complete fraud. Then again, When Faith and Football Teamed Up Against American Democracy is a microcosm of the incurious activism that dominates journalism these days. Its one thing to put up with relentless bias thats infected virtually every area of mainstream culture, but another to see once-respected magazines putting out such banal, predictable propaganda.

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Sports Illustrated Is Now A Bullhorn For Attacks On The First Amendment - The Federalist

Trump Plans to Appeal Dismissal of Twitter First Amendment Lawsuit in Ninth Circuit – Law & Crime

Former U.S. President Donald Trump gives the keynote address at the Faith & Freedom Coalition during their annual Road To Majority Policy Conference at the Gaylord Opryland Resort & Convention Center June 17, 2022 in Nashville, Tennessee.

Former PresidentDonald Trump has filed a notice that he will appealthe dismissal of his First Amendment lawsuit against Twitter, documents filed in appellate court reveal.

The 45th president and several additional plaintiffs including anti-vaccine advocates, COVID-19 misinformation spreaders, and conservative activists signaled their intent to appeal by filing a notice of appeal as well as a series of procedural exhibits in a Monday filing with the U.S. Court of Appeals for the Ninth Circuit.

In May of this year, the lawsuit was dismissed by U.S. District Judge James Donato, who found that the content moderation decisions made by Twitter in no way impacted the First Amendment because Twitter is not the government.

Plaintiffs main claim is that defendants have censor[ed] plaintiffs Twitter accounts in violation of their right to free speech under the First Amendment to the United States Constitution, the judge noted in an 18-page order. Plaintiffs are not starting from a position of strength. Twitter is a private company, and the First Amendment applies only to governmental abridgements of speech, and not to alleged abridgements by private companies.'

In an inline citation to case law, the opinion offers a basic distillation of First Amendment doctrine [emphasis in original]:

[T]he Free Speech Clause prohibits only governmental abridgement of speech. The Free Speech Clause does not prohibit private abridgment of speech.

The only possible way for Trump and the other plaintiffs to make a First Amendment claim against Twitter, Donato noted, was the narrowly-applied state action doctrine, which holds that government activity can be viewed as dominating a private activity to such an extent that its participants must be deemed to act with the authority of the government and, as a result, be subject to constitutional constraints. But that doctrine, the judge went on, is not an easy claim to make.

In response to Twitters eventually-granted motion to dismiss, Trump and the others plaintiffs were found not to have strongly argued that the state action doctrine actually applied to their case on the merits. Rather, the thrust of their argument, Donato noted, was a procedural pleading that the inquiry was too fact-intensive to be dealt with on a motion for summary judgment. The judge rejected that out of hand and then described Trumps pleaded facts.

From the May dismissal:

Twitter is said to have closed Mr. Trumps account because of the risk of further incitement of violence and threats to physical safety. Twitter closed plaintiff [Linda] Cuadross account due to a post about vaccines, and Dr. [Naomi] Wolfs account for vaccine misinformation; Plaintiff [Rafael] Barbozas account was closed after retweeting President Trump and other conservatives on January 6, 2021,; plaintiff [Dominick] Latella after he post[ed] positive messages about Republican candidates and President Trump, and plaintiff [Wayne Allen] Root for messages he posted related to COVID-19 and the 2020 election results.

If anything, these explanations indicate that Twitter acted in response to factors specific to each account, and not pursuant to a state rule of decision, the order granting Twitter motion to dismiss explained.

Trump and the others cited a handful of comments made by Democratic Party politicians to argue that the state was directing Twitters censorial moves, however, the judge noted, the comments of a handful of elected officials are a far cry from a rule that is actually being enforced by the state.

There is no way to allege with any degree of plausibility when, if ever, the comments voiced by a handful of members of Congress might become a law, or what changes such a law might impose on social media companies like Twitter, Donato went on.

The plaintiffs will soon have to re-litigate their theories of alleged state action in one of the nations traditionally most left-leaning and Free Speech-favoring appellate courts.

Substantive pleadings in the case stylized as Trump v. Twitter have yet to be filed.

The notice of appeal is available below:

[image via Seth Herald/Getty Images]

Have a tip we should know? [emailprotected]

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Trump Plans to Appeal Dismissal of Twitter First Amendment Lawsuit in Ninth Circuit - Law & Crime

Opinion | What Cassidy Hutchinson Said that Could Prove Trump’s Criminal Undoing – POLITICO

As Ive explained previously, it could be difficult to prove beyond a reasonable doubt that Trump had the corrupt state of mind needed to convict him, for example, of obstructing an official proceeding.

In addition, a prosecution of Trump for inciting violence would face a serious First Amendment hurdle. The Supreme Court has long held that only incitement to imminent unlawful action is sufficient. The speaker had to know that the crowd would immediately break the law.

Courts have routinely set this bar very high in the context of political speech because the First Amendment broadly protects speech of that type. A political statement by the president of the United States would be presumptively protected by the First Amendment.

But now we have Hutchinsons testimony that Trump said he didnt f---ing care that they have weapons. Theyre not here to hurt me and that they would be going to the Capitol later. This is precisely the sort of smoking gun evidence needed to prove that the person speaking meant to incite imminent violence.

The DOJ will understandably be concerned that the Supreme Court particularly the current court would find that Trumps speech was constitutionally protected by the First Amendment. But this evidence should be enough to make them at least consider an incitement prosecution. Before Hutchinsons testimony, an incitement prosecution would likely fail to clear the high First Amendment hurdle. Now, it is at least a close call and something DOJ should seriously consider.

And to be clear, Hutchinsons testimony would not be hearsay if offered by the DOJ at court against Trump. Statements by a party opponent are not considered hearsay, according to Federal Rule of Evidence 801(d)(2). In this case, Trump would be the DOJs party opponent in a criminal prosecution of Trump, and her testimony regarding Trumps statements could be used against him in court.

Hutchinson also provided testimony that gets DOJ closer to what they would need to prosecute Trump for obstructing an official proceeding. That charge requires corrupt intent. She testified that Trump tried to grab the steering wheel of his official vehicle (The Beast) when Secret Service agents refused to take him to the Capitol. She also testified that when an agent physically blocked Trump from seizing the wheel, Trump himself placed his hand on the agents clavicles, just under his neck.

Trumps failed attempt to go to the Capitol, in itself, would not be a criminal offense. But the episode inside The Beast would be powerful evidence of Trumps intent. Up until now, the picture that emerged of Trump was of someone who engaged in inaction while the Capitol was under attack, declining to call off his supporters or to call in police or troops. In itself, that is dereliction of duty, not a crime.

But episodes like trying to wrest the steering wheel show that Trump wanted to be at the Capitol and would have been there if he hadnt been kept from doing so. He wanted to be there, hands on, for the attack itself. That sheds a powerful light on his state of mind.

Juries are typically instructed to infer a defendants state of mind from his words and actions. In this situation, Trumps actions speak loudly, and they can be used as evidence of Trumps state of mind when he engaged in earlier actions.

Prosecutors will still need to put together a case that shows that Trump was involved in a conspiracy or scheme that obstructed the Jan. 6 certification proceeding. Thats not the simple task that many would have you believe. But its easier than establishing intent.

Hutchisons testimony is a game changer. Until now, the only readily provable crimes based on what is known publicly were potentially narrow criminal charges against crooked lawyers. Now it looks like an (otherwise unlikely) incitement prosecution is possible, and there may be the smoking gun needed for an obstruction charge.

The committee was smart to lock in public testimony from Hutchinson when it had the chance, given the potentially unlawful pressure against her to change her tune. Committee members have to hope that others follow in her footsteps. But they already have much of what they need.

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Opinion | What Cassidy Hutchinson Said that Could Prove Trump's Criminal Undoing - POLITICO

How to get started with machine learning and AI – Ars Technica

Enlarge / "It's a cookbook?!"

Aurich Lawson | Getty Images

Back in the 1950s, in the earliest days of what we now call artificial intelligence, there was a debate over what to name the field. Herbert Simon, co-developer of both the logic theory machine and the General Problem Solver, argued that the field should have the much more anodyne name of complex information processing. This certainly doesnt inspire the awe that artificial intelligence does, nor does it convey the idea that machines can think like humans.

However, "complex information processing" is a much better description of what artificial intelligence actually is: parsing complicated data sets and attempting to make inferences from the pile. Some modern examples of AI include speech recognition (in the form of virtual assistants like Siri or Alexa) and systems that determine what's in a photograph or recommend what to buy or watch next. None of these examples are comparable to human intelligence, but theyshow we can do remarkable things with enough information processing.

Whether we refer to this field as "complex information processing" or "artificial intelligence" (or the more ominously Skynet-sounding "machine learning") is irrelevant. Immense amounts of work and human ingenuity have gone into building some absolutely incredible applications. As an example, look atGPT-3, a deep-learning model for natural languages that can generate text that is indistinguishable from text written by a person (yet can also go hilariously wrong). It's backed by a neural network model that uses more than 170 billion parameters to model human language.

Built on top of GPT-3 is the tool named Dall-E,which will produce an image of any fantastical thing a user requests. The updated 2022 version of the tool, Dall-E 2, lets you go even further, as it can understand styles and concepts that are quite abstract.For instance, asking Dall-E to visualize an astronaut riding a horse in the style of Andy Warhol will produce a number of images such as this:

Dall-E 2 does not perform a Google search to find a similar image; it creates a picture based on its internal model. This is a new image built from nothing but math.

Not all applications of AI are as groundbreaking as these. AI and machine learning are finding uses in nearly every industry. Machine learning is quickly becoming a must-have in many industries, powering everything from recommendation engines in the retail sector to pipeline safety in the oil and gas industry and diagnosis and patient privacy in the health care industry. Not every company has the resources to create tools like Dall-E from scratch, so there's a lot of demand for affordable, attainable toolsets.The challenge of filling that demand has parallels to the early days of business computing, when computers and computer programs were quickly becoming the technology businesses needed.While not everyone needs to develop the next programming language or operating system, many companies want to leverage the power of these new fields of study, and they need similar tools to help them.

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How to get started with machine learning and AI - Ars Technica

Deep Dive Into Advanced AI and Machine Learning at The Behavox Artificial Intelligence in Compliance and Security Conference – Financial Post

Article content

MONTREAL On July 19th, Behavox will host a conference to share the next generation of artificial intelligence in Compliance and Security with clients, regulators, and industry leaders.

The Behavox AI in Compliance and Security Conference will be held at the company HQ in Montreal. With this exclusive in-person conference, Behavox is relaunching its pre-COVID tradition of inviting customers, regulators, AI industry leaders, and partners to its Montreal HQ to deep dive into workshops and keynote speeches on compliance, security, and artificial intelligence.

Were extremely excited to relaunch our tradition of inviting clients to our offices in order to learn directly from the engineers and data scientists behind our groundbreaking innovations, said Chief Customer Intelligence Officer Fahreen Kurji. Attendees at the conference will get to enjoy keynote presentations as well as Innovation Paddocks where you can test drive our latest innovations and also spend time networking with other industry leaders and regulators.

Keynote presentations will cover:

The conference will also feature Innovation Paddocks where guests will be able to learn more from the engineers and data scientists behind Behavox innovations. At this conference, Behavox will demonstrate its revolutionary new product Behavox Quantum. There will be test drives and numerous workshops covering everything from infrastructure for cloud orchestration to the AI engine at the core of Behavox Quantum.

Whats in it for participants?

Behavox Quantum has been rigorously tested and benchmarked against existing solutions in the market and it outperformed competition by at least 3,000x using new AI risk policies, providing a holistic security program to catch malicious, immoral, and illegal actors, eliminating fraud and protecting your digital headquarters.

Attendees at the July 19th conference will include C-suite executives from top global banks, financial institutions, and corporations with many prospects and clients sending entire delegations to the conference. Justin Trudeau, Canadian Prime Minister, will give the commencement speech at the conference in recognition/ celebration of the world leading AI innovations coming out of Canada.

This is a unique opportunity to test drive the product and meet the team behind the innovations as well as network with top industry professionals. Register here for the Behavox AI in Compliance and Security Conference.

About Behavox Ltd.

Behavox provides a suite of security products that help compliance, HR, and security teams protect their company and colleagues from business risks.

Through AI-powered analysis of all corporate communications, including email, instant messaging, voice, and video conferencing platforms, Behavox helps organizations identify illegal, immoral, and malicious behavior in the workplace.

Founded in 2014, Behavox is headquartered in Montreal and has offices in New York City, London, Seattle, Singapore, and Tokyo.

More information about the company is available at https://www.behavox.com/.

View source version on businesswire.com: https://www.businesswire.com/news/home/20220628006051/en/

Contacts

Press: media@behavox.com

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Deep Dive Into Advanced AI and Machine Learning at The Behavox Artificial Intelligence in Compliance and Security Conference - Financial Post

5 Top Deep Learning Trends in 2022 – Datamation

Deep learning (DL) could be defined as a form of machine learning based on artificial neural networks which harness multiple processing layers in order to extract progressively better and more high-level insights from data. In essence it is simply a more sophisticated application of artificial intelligence (AI) platforms and machine learning (ML).

Here are some of the top trends in deep learning:

Model Scale Up

A lot of the excitement in deep learning right now is centered around scaling up large, relatively general models (now being called foundation models). They are exhibiting surprising capabilities such as generating novel text, images from text, and video from text. Anything that scales up AI models adds yet more capabilities to deep learning. This is showing up in algorithms that go beyond simplistic responses to multi-faceted answers and actions that dig deeper into data, preferences, and potential actions.

Scale Up Limitations

However, not everyone is convinced that the scaling up of neural networks is going to continue to bear fruit. Roadblocks may lie ahead.

There is some debate about how far we can get in terms of aspects of intelligence with scaling alone, said Peter Stone, PhD, Executive Director, Sony AI America.

Current models are limited in several ways, and some of the community is rushing to point those out. It will be interesting to see what capabilities can be achieved with neural networks alone, and what novel methods will be uncovered for combining neural networks with other AI paradigms.

AI and Model Training

AI isnt something you plug in and, presto, instant insights. It takes time for the deep learning platform to analyze data sets, spot patterns, and begin to derive conclusions that have broad applicability in the real world. The good news is that AI platforms are rapidly evolving to keep up with model training demands.

Instead of weeks to learn enough to begin to function, AI platforms are undergoing fundamental innovation, and are rapidly reaching the same maturity level as data analytics. As datasets become larger, deep learning models become more resource-intensive, requiring a lot of processing power to predict, validate, and recalibrate millions of times. Graphics Processing Units (GPUs) are advancing to handle this computing and AI platforms are evolving to keep up with model training demands.

Organizations can enhance their AI platforms by combining open-source projects and commercial technologies, said Bin Fan, VP Open Source and Founding Engineer atAlluxio.

It is essential to consider skills, speed of deployment, the variety of algorithms supported, and the flexibility of the system while making decisions.

Containerized Workloads

Deep learning workloads are increasingly containerized, further supporting autonomous operations, said Fan. Container technologies enable organizations to have isolation, portability, unlimited scalability, and dynamic behavior in MLOps. Thus, AI infrastructure management would become more automated, easier, and more business-friendly than before.

Containerization being the key, Kubernetes will aid cloud-native MLOps in integrating with more mature technologies, said Fan.

To keep up with this trend, organizations can find their AI workloads running on more flexible cloud environments in conjunction with Kubernetes.

Prescriptive Modeling over Predictive Modeling

Modeling has gone through many phases over the last many years. Initial attempts tried to predict trends from historical data. This had some value, but didnt take into account factors such as context, sudden traffic spikes, and shifts in market forces. In particular, real-time data played no real part in early efforts at predictive modeling.

As unstructured data became more important, organizations wanted to mine it to glean insight. Coupled with the rise in processing power, suddenly real time analysis rose to prominence. And the immense amounts of data generated by social media has only added to the need to address real time information.

How does this relate to AI, deep learning, and automation?

Many of the current and previous industry implementations of AI have relied on the AI to inform a human of some anticipated event, who then has the expert knowledge to know what action to take, said Frans Cronje, CEO and Co-founder of DataProphet.

Increasingly, providers are moving to AI that can anticipate a future event and take the correspondent action.

This opens the door to far more effective deep learning networks. With real time data being constantly used by multi-layered neural networks, AI can be utilized to take more and more of the workload away from humans. Instead of referring the decision to a human expert, deep learning can be used to prescribe predicted decisions based on historical, real-time, and analytical data.

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5 Top Deep Learning Trends in 2022 - Datamation

How AI and Machine Learning Are Ready To Change the Game for Data Center Operations – Data Center Knowledge

Todays data centers face a challenge that, initially, looks like its almost impossible to resolve. While operations have never been busier, teams are pressured to reduce their facilities energy consumption as part of corporate carbon reduction goals. And, as if that wasnt difficult enough, dramatically rising electricity prices are placing real stress on data center budgets.

With data centers focused on supporting the essential technology services that people increasingly demand to support their personal and professional lives, its not surprising that data center operations have never been busier. Driven by trends that show no sign of slowing down, were seeing massively increased data usage associated with video, storage, compute demands, smart IoT integrations, as well as 5G connectivity rollouts. However, despite these escalating workloads, the unfortunate reality is that many of todays critical facilities simply arent running efficiently enough.

Given that the average data center operates for over 20 years, this shouldnt really be a surprise. Efficiency is invariably tied to a facilitys original design - and based on expected IT loads that have long been overtaken. At the same time change is a constant factor, with platforms, equipment design, topologies, power density requirements and cooling demands all evolving with the continued drive for new applications. The result is a global data center infrastructure that regularly finds it hard to match current and planned IT loads to their critical infrastructure. This will only be exacerbated as data center demands increase, with analyst projections suggesting that workload volumes are set to continue growing at around 20% a year between now and 2025.

Traditional data center approaches are struggling to meet these escalating requirements. Prioritizing availability is largely achieved at efficiencys expense, with too much reliance still placed on operator experience and trusting that assumptions are correct. Unfortunately, the evidence suggests that this model is no longer realistic. EkkoSense research reveals an average figure of 15% of IT racks in data centers operating outside of ASHRAEs temperature and humidity guidelines, and that customers strand up to 60% of their cooling capacity due to inefficiencies. And thats a problem, with Uptime Institute estimating that the global value attributed to inefficient cooling and airflow management is around $18bn. Thats equivalent to some 150bn wasted kilowatt hours.

With 35% of the energy used in a data center utilized to support the cooling infrastructure, its clear that traditional performance optimization approaches are missing a huge opportunity to unlock efficiency improvements. EkkoSense data indicates that a third of unplanned data center outages are triggered by thermal issues. Finding a different way to manage this problem can provide operations teams with a great way to secure both availability and efficiency improvements.

Limitations of traditional monitoringUnfortunately, only around 5% of M&E teams currently monitor and report their data center equipment temperatures on a rack-by-rack basis. Additionally, DCIM and traditional monitoring solutions can provide trend data and be set up to provide alerts when breaches occur, but that is where they stop. They lack the analytics to provide deeper insite into the cause of the issues and how both to resolve them and avoid them in the future.

Operations teams recognize that this kind of traditional monitoring has its limitations, but they also know that they simply dont have the resources and time to take the data they have and convert it from background noise into meaningful actions. The good news is that technology solutions are now available to help data centers tackle this problem.

It's time for data centers to go granular with machine learning and AIThe application of machine learning and AI creates a new paradigm in terms of how to approach data center operations. Instead of being swamped by too much performance data, operations teams can now take advantage of machine learning to gather data at a much more granular level meaning they can start to access how their data center is performing in real-time. The key is to make this accessible, and using smart 3D visualizations is a great way of making it easy for data center teams to interpret performance data at a deeper level: for example, by showing changes and highlighting anomalies.

The next stage is to apply machine learning and AI analytics to provide actionable insights. By augmenting measured datasets with machine learning algorithms, data center teams can immediately benefit from easy-to-understand insights to help support their real-time optimization decisions. The combination of real-time granular data collection every five minutes and AI/machine learning analytics allows operations not just to see what is happening across their critical facilities but also find out why and what exactly they should do about it.

AI and machine learning powered analytics can also uncover the insights required to recommend actionable changes across key areas such as optimum set points, floor grille layouts, cooling unit operation and fan speed adjustments. Thermal analysis will also indicate optimum rack locations. And because AI enables real-time visualizations, data center teams can quickly gain immediate performance feedback on any actioned changes.

Helping data center operations to make an immediate difference Given pressure to reduce carbon consumption and minimize the impact of electricity price increases, data center teams need new levels of optimization support if they are to deliver against their reliability and efficiency goals.

Taking advantage of the latest machine learning and AI-powered data center optimization approaches can certainly make a difference by cutting cooling energy and usage with results achievable within weeks. Bringing granular data to the forefront of their optimization plans, data center teams have already been able to not only remove thermal and power risk, but also secure reductions in cooling energy consumption costs and carbon emmissions by an average of 30%. Its hard to ignore the impact these kind of savings can have particularly during a period of rapid electricity price increases. The days of trading off risk and availability for optimization is a thing of the past with power of AI and Machine learning at the forefront of operating your data center.

Related: Scale Your Machine Learning with MLOps

Want to know more? Register for Wednesday's AFCOMwebinar on the subject here.

About the author

Tracy Collins is Vice President of EkkoSense Americas, the company that enables true M&E capacity planning for power, cooling and space. He was previously CEO at Simple Helix, a leading AL-based Tier III data center operator.

Tracy has over 25 years in-depth data center industry experience, having previously served as Vice President of IT Solutions for Vertiv and, before that, with Emerson Network Power. In his role, Tracy is committed to challenging traditional approaches to data center management particularly in terms of solving the optimization challenge of balancing increased data center workloads while also delivering against corporate energy saving targets.

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How AI and Machine Learning Are Ready To Change the Game for Data Center Operations - Data Center Knowledge

Companies In The Lawful Interception Market Are Adopting AI, Machine Learning, And Blockchain Technologie – Benzinga

LONDON, June 28, 2022 (GLOBE NEWSWIRE) -- According to The Business Research Company's research report on the lawful interception market, leveraging Artificial Intelligence (AI), machine learning, and blockchain technologies for cyber defense is a key trend in the lawful interception market. Lawful interception providers integrate AI and machine learning principles into their solutions to tackle crucial hyper-connected workplace risks with quicker identification, prevention, and responsive capabilities. Advances in technology, such as AI and machine learning, turn the tables on cybercrime. For example, Equifax experienced cyber-attacks which resulted in the loss of sensitive information from more than 140 million American customers. The stolen information included names, addresses, social security numbers, birth dates, and driver's license numbers. Cybersecurity specialists are therefore leveraging AI and machine learning technology to resolve the emerging cyber threats facing individuals, companies, and governments. According to a recent Research and Markets study, the demand for artificial intelligence in cyber security is expected to reach $38.2 billion by 2026.

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The global lawful interception market size is expected to grow from $2.96 billion in 2021 to $3.57 billion in 2022 at a compound annual growth rate (CAGR) of 20.64%. The growth in the market is mainly due to the companies resuming their operations and adapting to the new normal while recovering from the COVID-19 impact, which had earlier led to restrictive containment measures involving social distancing, remote working, and the closure of commercial activities that resulted in operational challenges. The global lawful interception market share is expected to reach $7.86 billion in 2026 at a CAGR of 21.84%.

The increasing number of cybercrimes is expected to propel the growth of the lawful interception market. Cybercrimes are defined as the increasing number of cyber-attacks through various social media platforms, the internet, and hacking software. The increased cybercrimes are responsible for the growth of lawful interceptions as they are a key tool for identifying crimes. As per the Internet Crime Report 2021, published by the Federal Bureau of Investigation (FBI) in the U.S., there were approximately 791,790 complaints of suspected internet crimean increase of more than 300,000 complaints from 2019. For instance, in 2021, Tessian Research, a data loss prevention (DLP) on email company, found that employees received an average of 14 malicious emails per year. Phishing is the most popular type of cybercrime in which criminals seek to gain sensitive information by sending phone emails or messages. Therefore, increasing cybercrimes drive the lawful interception market.

Major players in the lawful interception market are Utimaco, Vocal Technologies, AQSACOM, Verint, BAE Systems, SS8 Networks, Signalogic, IPS S.P.A, Tracespan, Accuris Networks, EVE Compliancy Solutions, Squire Technologies, Incognito Software, Incognito Software, Net Optics, and Ixia.

The global lawful interception market analysis is segmented by device into mediation devices, routers, intercept access point (IAP), gateways, switches, management servers, others; by network technology into Voice-Over-Internet Protocol (VoIP), Long Term Evolution (LTE), Wireless Local Area Network (WLAN), Worldwide Interoperability for Microwave Access (WiMAX), Digital Subscriber Line (DSL), Public Switched Telephone Network (PSTN), Integrated Services for Digital Network (ISDN), others; by communication content into voice communication, video, text messaging, facsimile, digital pictures, file transfer; by end user into lawful enforcement agencies, government.

North America was the largest region in the lawful interception market in 2021. Asia Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the lawful interception industry report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, the Middle East, and Africa.

Lawful Interception Global Market Report 2022 Market Size, Trends, And Global Forecast 2022-2026 is one of a series of new reports from The Business Research Company that provide lawful interception market overviews, lawful interception market analyze and forecast market size and growth for the whole market, lawful interception market segments and geographies, lawful interception market trends, lawful interception market drivers, lawful interception market restraints, lawful interception market leading competitors' revenues, profiles and market shares in over 1,000 industry reports, covering over 2,500 market segments and 60 geographies.

The report also gives in-depth analysis of the impact of COVID-19 on the market. The reports draw on 150,000 datasets, extensive secondary research, and exclusive insights from interviews with industry leaders. A highly experienced and expert team of analysts and modelers provides market analysis and forecasts. The reports identify top countries and segments for opportunities and strategies based on market trends and leading competitors' approaches.

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Companies In The Lawful Interception Market Are Adopting AI, Machine Learning, And Blockchain Technologie - Benzinga

Revisiting the trial of Julian Assange | Julian Assange News | Al Jazeera

PodcastPodcast, The Take

Julian Assanges long battle against extradition.

Julian Assange has waged a long fight against extradition from the United Kingdom to the United States, and after years, a final decision is imminent. But when former UN Special Rapporteur on Torture Nils Melzer was asked to look into his case in 2018, he found himself surprisingly uninterested. One allegation after another had come to cloud the narrative of Assange, liberator of state secrets. But Melzer has since investigated them all and he discovered that the level of deception is staggering.

In this episode:

Nils Melzer, author of The Trial of Julian Assange (@NilsMelzer)

This episode first ran in January 2022 and was updated by Alexandra Locke. The original production team was Alexandra Locke, Amy Walters, Negin Owliaei, Priyanka Tilve, Ruby Zaman, Ney Alvarez, Tom Fenton, Stacey Samuel, and Malika Bilal. Alex Roldan is our sound designer. Aya Elmileik and Adam Abou-Gad are our engagement producers.

Connect with us at @AJEPodcasts on Twitter, Instagram, and Facebook.

See the article here:

Revisiting the trial of Julian Assange | Julian Assange News | Al Jazeera