Artificial Intelligence Could Help Scientists Predict Where And When Toxic Algae Will Bloom – WBUR

Climate-driven change in the Gulf of Maine is raising new threats that "red tides" will become more frequent and prolonged. But at the same time, powerful new data collection techniques and artificial intelligence are providing more precise ways to predict where and when toxic algae will bloom. One of those new machine learning prediction models has been developed by a former intern at Bigelow Labs in East Boothbay.

In a busy shed on a Portland wharf, workers for Bangs Island Mussels sort and clean shellfish hauled from Casco Bay that morning. Wholesaler George Parr has come to pay a visit.

"I wholesale to restaurants around town, and if there's a lot of mackerel or scallops, I'll ship into Massachusetts," he says.

Butbusiness grinds to a halt, he says, when blooms of toxic algae suddenly emerge in the bay causing the dreaded red tide.

Toxins can build in filter feeders to levels that would cause "Paralytic Shellfish Poisoning" in human consumers. State regulators shut down shellfish harvests long before danger grows acute. But when a red tide swept into Casco Bay last summer, Bangs Island's harvest was shut down for a full 11 weeks.

So when the restaurants can't get Bangs Island they're like 'Why can't we get Bangs Island?' It was really bad this summer. And nobody was happy."

As Parr notes, businesses of any kind hate unpredictability. And being able to forecast the onset or departure of a red tide has been a challenge although that's changing with the help of a type of artificial intelligence called machine learning.

"We're coming up with forecasts on a weekly basis for each site. For me that's really exciting. That's what machine learning is bringing to the table," says Izzi Grasso, a recent Southern Maine Community College student who is now seeking a mathematics degree at Clarkson University.

Last summer Grasso interned at the Bigelow Laboratory for Ocean Sciences in East Boothbay. That's where she helped to lead a successful project to use cutting-edge "neural network" technology that is modeled on the human brain to better predict toxic algal blooms in the Gulf of Maine.

"Really high accuracy. Right around 95 percent or higher, depending on the way you split it up," she says.

Here's how the project worked: the researchers accessed a massive amount of data on toxic algal blooms from the state Department of Marine Resources. The data sets detailed the emergence and retreat of varied toxins in shellfish samples from up and down the coast over a three-year period.

The researchers trained the neural network to learn from those thousands of data points. Then it created its own algorithms to describe the complex phenomena that can lead up to a red tide.

Then we tested how it would actually predict on unknown data, says Grasso.

Grasso says they fed in data from early 2017 which the network had never seen and asked it to forecast when and where the toxins would emerge.

"I wasn't surprised that it worked, but I was surprised how well it worked, the level of accuracy and the resolution on specific sites and specific weeks," says Nick Record, Bigelow's big data specialist.

Record says that the network's accuracy, particularly in the week before a bloom emerges, could be a game-changer for the shellfish industry and its regulators.

Once it's ready, that is.

"Basically it works so well that I need to break it as many ways as I can before I really trust it."

Still, the work has already been published in a peer-reviewed journal, and it is getting attention from the scientific community. Don Anderson is a senior scientist at the Woods Hole Oceanographic Institution who is working to expand the scope of data-gathering efforts in the Gulf.

"The world is changing with respect to the threat of algal blooms in the Gulf of Maine," he says. "We used to worry about only one toxic species and human poisoning syndrome. Now we have at least three."

Anderson notes, though, that machine-learning networks are only as good as the data that is fed into them. The Bigelow network, for instance, might not be able to account for singular oceanographic events that are short and sudden or that haven't been captured in previous data-sets such as a surge of toxic cells that his instruments detected off Cutler last summer.

"With an instrument moored in the water there, and we in fact got that information, called up the state of Maine and said you've got to be careful, there's a lot of cells moving down there, and they actually had a meeting, they implemented a provisional closure just on the basis of that information, which was ultimately confirmed with toxicity once they measured it," says Anderson.

Anderson says that novel modeling techniques such as Bigelow's, coupled with an expanded number of high-tech monitoring stations, like Woods Hole is pioneering in the Gulf, could make forecasting toxic blooms as simple as checking the weather report.

"That situational awareness is what everyone's striving to produce in the field of monitoring and management of these toxic algal blooms, and it's going to take a variety of tools, and this type of artificial intelligence is a valuable part of that arsenal." Back at the Portland wharf, shellfish dealer George Parr says the research sounds pretty promising.

"Forewarned is fore-armed, Parr says. If they can figure out how to neutralize the red tide, that'd be even better."

Bigelow scientists and former intern Izzi Grasso are working now to look "under the hood" of the neural network, to figure out how, exactly, it arrives at its conclusions. They say that could provide clues about how not only to predict toxic algal blooms,but even how to prevent them.

This story is a production of New England News Collaborative. A version of this story was originallypublishedby Maine Public Radio.

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The Ethical Upside to Artificial Intelligence – War on the Rocks

According to some, artificial intelligence (AI) is thenew electricity. Like electricity, AI will transform every major industry and open new opportunities that were never possible. However, unlike electricity, the ethics surrounding the development and use of AI remain controversial, which is a significant element constraining AIs full potential.

The Defense Innovation Board (DIB) released a paper in October 2019 that recommends the ethical use of AI within the Defense Department. It described five principles of ethically used AI responsible, equitable, traceable, reliable, and governable. The paper also identifies measures the Joint Artificial Intelligence Center, Defense Agency Research Projects Agency (DARPA), and U.S. military branches are taking to study the ethical, moral, and legal implications of employing AI. While the paper primarily focused on the ethics surrounding the implementation and use of AI, it also argued that AI must have the ability to detect and avoid unintended harm. This article seeks to expand on that idea by exploring AIs ability to operate within the Defense Department using an ethical framework.

Designing an ethical framework a set of principles that guide ethical choice for AI, while difficult, offers a significant upside for the U.S. military. It can strengthen the militarys shared moral system, enhance ethical considerations, and increase the speed of decision-making in a manner that provides decision superiority over adversaries.

AI Is Limited without an Ethical Framework

Technology is increasing the complexity and speed of war. AI, the use of computers to perform tasks normally requiring human intelligence, can be a means of speeding decision-making. Yet, due to a fear of machines inability to consider ethics in decisions, organizations are limiting AIs scope to focus ondata-supported decision-making using AI to summarize data while keeping human judgment as the central processor. For example, leaders within the automotive industry received backlash for programming self-driving cars to make ethical judgments. Some professional driving organizations have demanded that these cars be banned from the roads for at least 50 years.

This backlash, while understandable, misses the substantial upside that AI can offer to ethical decision-making. AI reflectshuman inputand operates on human-designed algorithms that set parameters for the collection and correlation of data to facilitate machine learning. As a result, it is possible to build an ethical framework that reflects a decision-makers values. Of course, when the data that humans supply is biased, for example, AI can mimic its trainers bydiscriminating on gender and race. Biased algorithms, to be sure, are a drawback. However, bias can be mitigated by techniques such as counterfactual fairness, Google AIs recommended practices, and algorithms such as those provided by IBMs AI Fairness 360 toolkit. Moreover, AI processing power makes it essential for successfully navigating ethical dilemmas in a military setting, where complexity and time pressure often obscure underlying ethical tensions.

A significant obstacle to building an ethical framework for AI is a fundamental element of war the trade-off between human lives and other military objectives. While international humanitarian law provides a codification of actions, many of which have ethical implications, it does not answer all questions related to combat. It primarily focuses on defining combatants, the treatment of combatants and non-combatants, and acceptable weapons. International humanitarian law does not deal with questions concerning how many civilian deaths are acceptable for killing a high-valued target, or how many friendly lives are worth sacrificing to take control of a piece of territory. While, under international law, these are examples of military judgments, this remains an ethical decision for the military leader responsible.

Building ethical frameworks into AI will help the military comply with international humanitarian law and leverage new opportunities while predicting and preventing costly mistakes in four ways.

Four Military Benefits of an Ethical AI Framework

Designing an ethical framework for AI will benefit the military by forcing its leaders to reexamine existing ethical frameworks. In order to supply the benchmark data on which AI can learn, leaders will need to define, label, and score choice options in ethical dilemmas. In doing so they will have three primary theoretical frameworks to leverage for guidance: consequentialist, deontological, and virtue. While consequentialist ethical theories focus on the consequences of the decision (e.g., expected lives saved), deontological ethical theories are concerned with the compliance with a system of rules (refusing to lie based on personal beliefs and values despite the possible outcomes). Virtue ethical theories are concerned with instilling the right amount of a virtuous quality into a person (too little courage is cowardice; too much is rashness; the right amount is courage). A common issue cited as anobstacle to machine ethicsis the lack of agreement on which theory or combination of theories to follow leaders will have to overcome this obstacle. This introspection will help them better understand their ethical framework, clarify and strengthen the militarys shared moral system, andenhance human agency.

Second, AI can recommend decisions that consistently reflect a leaders preferred ethical decision-making process. Even in high-stakes situations, human decision-making is prone to influence from factors that have little or nothing to do with the underlying choice. Things like poor nutrition, fatigue, and stress all common in warfare can lead to biased and inconsistent decision-making. Other influences, such as acting in ones self-interest or extreme emotional responses, can also contribute tomilitary members making unethical decisions. AI, of course, does not become fatigued or emotional. The consistency of AI allows it to act as a moral adviser by providing decision-makers morally relevant data leaders can rely on as their judgment becomes impaired. Overall, this can increase the confidence of young decision-makers, a concern thecommander of U.S. Army Training and Doctrine Commandbrought up early last year.

Third, AI can help ensure that U.S. military leaders make the right ethical choice however they define that in high-pressure situations. Overwhelming the adversary is central to modern warfare. Simultaneous attacks anddeception operationsaim to confuse decision-makers to the point where they can no longer use good judgment. AI can process and correlate massive amounts of data to provide not only response options, but also probabilities that a given option will result in an ethically acceptable outcome. Collecting battlefield data, processing the information, and making an ethical decision is very difficult for humans in a wartime environment. Although the task would still be extremely difficult, AI can gather and process information more efficiently than humans. This would be valuable for the military. For example, AI that is receiving and correlating information from sensors across the entire operating area could estimate non-combatant casualties, the proportionality of an attack, or social reactions from observing populations.

Finally, AI can also extend the time allowed to make ethical decisions in warfare. For example, a central concern in modern military fire support is the ability to outrange the opponent, to be able to shoot without being shot. The race to extend the range of weapons to outpace adversaries continues to increase the time between launch and impact. Future warfare will see weapons that are launched and enter an area that is so heavily degraded and contested that the weapon will lose external communication with the decision-maker who chose to fire it. Nevertheless, as the weapon moves closer to the target, it could gain situational awareness on the target area and identify changes pertinent to the ethics of striking a target. If equipped with onboard AI operating with an ethical framework, the weapon could continuously collect, correlate, and assess the situation throughout its flight to meet the parameters of its programmed framework. If the weapon identified a change in civilian presence or other information altering the legitimacy of a target, the weapon could divert to a secondary target, locate a safe area to self-detonate, or deactivate its fuse. This concept could apply to any semi- or fully autonomous air, ground, maritime, or space assets. The U.S. military could not afford a weapon system deactivating or returning to base in future conflicts each time it loses communication with a human. If an AI-enabled weapon loses the ability to receive human input, for whatever reason, an ethical framework will allow the mission to continue in a manner that aligns the weapons actions with the intent of the operator.

Conclusion

Building an ethical framework for AI will help clarify and strengthen the militarys shared moral system. It will allow AI to act as a moral adviser and provide feedback as the judgment of decision-makers becomes impaired. Similarly, an ethical framework for AI will maximize the utility of its processing power to help ensure ethical decisions when human cognition is overwhelmed. Lastly, providing AI an ethical framework can extend the time available to make ethical decisions. Of course, AI is only as good as the data it is provided.

AI should not replace U.S. military leaders as ethical decision-makers. Instead, if correctly designed, AI should clarify and amplify the ethical frameworks that U.S. military leaders already bring to war. It should help leaders grapple with their own moral frameworks, and help bring those frameworks to bear by processing more data than any decision-maker could, in places where no decision-maker could go.

AI may create new programming challenges for the military, but not new ethical challenges. Grappling with the ethical implications of AI will help leaders better understand moral tradeoffs inherent in combat. This will unleash the full potential of AI, and allow it to increase the speed of U.S. decision-making to a rate that outpaces its adversaries.

Ray Reeves is a captain in the U.S. Air Force and a tactical air control party officer and joint terminal attack controller (JTAC) instructor and evaluator at the 13thAir Support Operations Squadron on Fort Carson, Colorado. He has multiple combat deployments and is a doctoral student at Indiana Wesleyan University, where he studies organizational leadership. The views expressed here are his alone and do not necessarily reflect those of the U.S. government or any part thereof. Linkedin.

Image: U.S. Marine Corps (Photo by Lance Cpl. Nathaniel Q. Hamilton)

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Seizing Artificial Intelligence’s Opportunities in the 2020s – AiThority

Artificial Intelligence (AI) has made major progress in recent years. But even milestones like AlphaGo or the narrow AI used by big tech only scratch the surface of the seismic changes yet to come.

Modern AI holds the potential to upend entire profession while unleashing brand new industries in the process. Old assumptions will no longer hold, and new realities will dictate those who are swallowed by the tides of change from those able to anticipate and ride the AI wave headlong into a prosperous future.

Heres how businesses and employees can both leverage AI in the 2020s.

Like many emerging technologies, AI comes with a substantial learning curve. As a recent McKinsey report highlights, AI is a slow burn technology that requires a heavy upfront investment, with returns only ramping up well down the road.

Because of this slow burn, an AI front-runner and an AI laggard may initially appear to be on equal footing. The front-runner may even be a bit behind during early growing pains. But as the effects of AI adoption kick in, the gap between the two widens dramatically and exponentially. McKinseys models estimate that within around 10 years, the difference in cumulative net change in cash flow between front-runners and laggards could be as high as 145 percent.

The first lesson for any business hoping to seize new AI opportunities is to start making moves to do so right now.

Read More: How is Artificial Intelligence (AI) Changing the Future of Architecture?

Despite popular opinion, the coming AI wave will be mostly a net positive for employees. The World Economic Forum found that by 2022, AI and Machine Learning will have created over 130 million new jobs. Though impressive, these gains will not be distributed evenly.

Jobs characterized by unskilled and repetitive tasks face an uncertain future, while jobs in need of greater social and creative problem-solving will spike. According to McKinsey, the coming decade could see a 10 percent fall in the share of low digital skill jobs, with a corresponding rise in the share of jobs requiring high digital skill.

So how can employees successfully navigate the coming future of work? One place to start is to investigate the past. Nearly half a century ago, the first ATM was installed outside Barclays Bank in London. In 1967, the thought of bank tellers surviving the introduction of automated teller machines felt impossible. ATMs caught on like wildfire, cut into tellers hours, offered unbeatable flexibility and convenience, and should have all but wiped tellers out.

But, in fact, exactly the opposite happened. No longer having to handle simple deposits freed tellers up to engage with more complex and social facets of the business. They started advising customers on mortgages and loans, forging relationships and winning loyalty. Most remarkable of all, in the years following the ATMs introduction, the total number of tellers employed worldwide didnt fall off a cliff. In fact, it rose higher than ever.

Though AI could potentially threaten some types of jobs, many jobs will see rising demand. Increased reliance on automated systems for core business functions, frees up valuable employee time and enables them to focus on different areas to add even more value to the company.

As employees grow increasingly aware of the changing nature of work, they are also clamoring for avenues for development, aware that they need to hold a variety of skills to remain relevant in a dynamic job market. Companies will, therefore, need to provide employees with a wide range of experiences and the opportunity to continuously enhance their skillsets or suffer high turnover. This is already a vital issue to businesses with the cost of losing an employee equating to 90%-200% of their annual salary. This costs each large enterprise an estimated $400 million a year. If employees feel their role is too restrictive or that their organization is lagging, their likelihood of leaving will climb.

The only way to capture the full value of AI for business is to retain the highly skilled employees capable of wielding it. Departmental silos and rigid job descriptions will have no place in the AI future.

Read More: How Artificial Intelligence and Blockchain is Revolutionizing Mobile Industry in 2020?

For employees to maximize their chances of success in the face of rapid AI advancement, they must remain flexible and continuously acquire new skills. Both businesses and employees will need to realign their priorities in accordance with new realities. Workers will have to be open to novel ideas and perspectives, while employers will need to embrace the latest technological advancements.

Fortunately, the resources and avenues for ambitious employers to pursue continued growth for their employees are blossoming. Indeed, the very AI advancements prompting the need for accelerated career development paths are also powering technologies to maximize and optimize professional enrichment.

AI is truly unlocking an exciting new future of work. Smart algorithms now enable hyper-flexible workplaces to seamlessly shuffle and schedule employee travel, remote work, and mentorship opportunities. At the cutting edge, these technologies can even let employees divide their time between multiple departments across their organization. AI can also tailor training and reskilling programs to each employees unique goals and pace.

The rise of AI holds the promise of great change, but if properly managed, it can be a change for the better.

Read More: Predictions of AI AdTech in 2020

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How Automation and Artificial Intelligence Can Boost Cybersecurity – Robotics and Automation News

Cybercriminals are always evolving their efforts and coming up with more advanced ways to target their victims. And while there are many tools available to stop them, there is a lot of space for improvement. Especially if you take automation into account.

Machine learning and artificial intelligence are playing a significant role in cybersecurity. Automation tools can prevent, detect, and deal with tons of cyber threats way more efficiently and faster than humans. And it will continue to expand down the road. To that end, heres a quick look at the significant differences AI/ML technologies can make to corporate cybersecurity approaches.

Mitigating the risks posed by omnipresent technology

Technology has permeated every facet of personal and work lives. Above anything else, it increased their attack surface. And it has become a massive problem for companies in recent years they have to account for many applications and devices.

The problem is, there arent enough skilled human resources to contend with all those security risks. Thats why it often results in gaping vulnerabilities.

To add to that problem, many companies cannot afford having cybersecurity teams needed to secure their applications and systems. Startups, in particular, are at risk. They lack established security operations and the funds to ensure them.

Companies need to automate at least some of the processes necessary to protect their systems and devices from outside attacks. Otherwise, they stay vulnerable.

Criminals are using every tool at their disposal to make sure they have as many points of entry as possible. For example, not even firewalls can protect a system like they used to before, as criminals keep inventing new ways to get around them.

Theres no way to manually contend with this because theyre using automated methods to test the defenses of every connected device.

Better threat detection and management

The size of attacks and vast amounts of data available to analyze makes keeping up with the latest threats a challenging task. Automated machine learning applications are much more suited to constant vigilance and systematic threat identification.

These systems are learning all the time. They can evolve alongside growing threat vectors to spot unusual behaviors. It allows them to identify and process sophisticated attack methods.

But most companies are not making use of these game-changing technologies. They continue to rely on outdated methods. Yet conventional tools and applications cannot keep up with ill-intentioned actors. They keep leveraging more complicated capabilities in their attacks.

Cases such as the Outlaw cryptojacking attacks prove that hackers know how to use new technology to avoid detection. And they are quite successful in their endeavors. The only way to cope with such an onslaught of threats is through machine learning/artificial intelligence engines. They overlook the systems and alert about any suspicious and unusual behaviour.

Automating mundane cybersecurity processes

Many tools exist to cover the security needs of businesses. For example, most companies ask their employees to use virtual private networks (VPNs). (What is a VPN? Its a service that encrypts users connections to the internet (https://nordvpn.com/what-is-a-vpn/).

A tool like that makes sure outsiders cant intercept any data user is transferring over the network.) And while that covers the data in transfer, theres still a risk employees will fall for phishing emails or install ransomware by accident.

Security researchers cannot keep up with the threat alert notification overload. And many of these notifications are usually false. But you cant ignore them. Criminals know how to hide in all that noise. It makes threat identification a monumental task for security operation teams.

Thus, providing information security specialists with automated tools is essential. It lets them focus their skills in areas where theyre most needed. The mundane everyday tasks take up so much of technicians time.

But automation tools are capable of handling them. It frees time for more valuable tasks that need a human touch. For instance, threat hunting and attribution.

Considerable increase in risk

The world has grown to incorporate technology into almost every facet of daily life and with that comes a considerable increase in risk. Therefore, machine learning and artificial intelligence have become an indispensable part of cybersecurity.

They fulfil a vital role that human labor simply cant. Automation is the answer. It can help cybersecurity specialists to tackle the sheer number of cyberthreats in corporate and personal applications.

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M’sian courts to go digital and adopt artificial intelligence initiatives – The Star Online

KUALA LUMPUR (Bernama): The country's courts are not only going digital but are also adopting artificial intelligence (AI) initiatives to ensure easy access to justice.

Minister in the Prime Minister's Department Datuk Liew Vui Keong said the government was already pursuing an array of AI initiatives in digitalising the courts.

This includes the introduction of e-bail and e-review that seek to reduce the need for lawyers and litigants to physically appear in court, saving time and costs for all parties, and digital voice to text court recording transcripts and digitally secured evidence.

"Through 2020, the government will continue to pursue and introduce additional AI initiatives to digitalise the courts and secure easy access to justice for all, he said in a statement here on Monday (Jan 20).

"The legal profession must embrace digitalisation, in which the Sabah and Sarawak Judiciary have (sic) led an exemplary path for legal practitioners across Malaysia to follow.

"I am delighted to hear the judiciarys support of the governments efforts to digitalise the courts through use of AI and information technology (IT). Support from the nations top judges was crucial. I therefore wish to record the governments sincerest appreciation for the tremendous support of the Chief Justice and Chief Judge of Sabah and Sarawak for these initiatives, he said.

These digital initiatives would not only facilitate easy access to justice by removing the necessity for the physical presence of parties in court but would also be environmentally friendly as they seek to reduce the usage of paper and carbon footprints incurred in travel, Liew said.- Bernama

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IT Teams Need More Than Password Managers – Security Boulevard

IT departments need more than a password manager to keep themand a companys datasafe from cyberthreats

All companies today are, to some extent, dependent on technology and the IT teams driving their systems and security in the background. These IT administrators, of course, have privileges to modify system or application configurations, install or remove software, make changes to the operating system, and more. Most companies only use a simple password management app to manage all passwords, including for these privileged admin accounts. Sadly, this is no longer sufficient to protect them from malicious insiders, cybercriminals and hackers.

Before we get into why theyre not sufficient, lets first talk about the typical capabilities of a password management solution. A password manager is a good way to begin securing general accounts such as NetFlix, Amazon, social media accounts, bank accounts, marketing tools such as Google Analytics and other apps. It helps users to consolidate passwords into a centralized vault, manage logins and streamline access to shared general accounts. However, when we look at any high-profile data breachessuch as those that occurred at Target, Marriott and Sonywe see cybercriminals primarily target privileged accounts. These could be local admin accounts, privileged user accounts, domain administrative accounts or service accounts, all of which are usually scattered across the companys internal IT infrastructure.

Apart from using password-based authentication for IT systems, some companies (especially those in finance, high-tech and government) prefer using secure shell (SSH) keys to protect their privileged accounts. Most companies leave these privileged accounts unmanaged or orphaned, and only a handful of privileged accounts are stored in the password management app. According to the 2019 Data Breach Investigations Report by Verizon, privilege abuse is one of the most common threats in data breaches.

This Verizon report offers crucial perspectives on threats that organizations face. It is built on real-world data from 41,686 security incidents and 2,013 data breaches provided by 73 data sources, both public and private entities, spanning 86 countries worldwide. Remember the American whistle-blower Edward Snowden, who breached the National Security Agency (NSA)? He simply used this privileged account management loophole to gain access to one of the worlds most advanced and sophisticated security agencies. Similarly, once cybercriminals get access to a privileged account, they can eventually gain access to all of the organizations sensitive information, deploy remote access tools, steal as much data as possible and even may perform financial fraud.

A password manager can work well for many departments including marketing, finance and human resources. However, your IT teams need a comprehensive privileged account management (PAM) solution to protect your companys IT infrastructure in this era of cyberattacks. Your typical privileged account management solution can:

These important differences between a password manager and a fully-featured PAM solution could be the key to protecting your organizations information. According to leading research firm Gartner, privileged account management is also the number one security priority for chief information security officers (CISOs). Implementing a PAM solution alone may not help you to keep hackers at baythere is always more to be done. However, a PAM solution will provide you with a solid foundation to continue building your defenses against cybercriminals.

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IT Teams Need More Than Password Managers - Security Boulevard

Opinion | Pete Buttigieg Says He’s More Than a Resume – The New York Times

This interview was conducted by the editorial board of The New York Times, which will announce its Democratic primary endorsement on Jan. 19. For noteworthy dialogues on...

Well, thanks for having me over.

Kathleen Kingsbury: Thank you for coming. So, we have heard you obviously talk about health care and climate and the Middle East a lot in the debates, so were going to try to ask you some questions we havent heard you answer in the past, and you will be shocked to hear that wed like to start with your time at McKinsey. You graduated from Oxford with sterling credentials. You could have pursued any number of career paths from there, including the choice you ultimately made to join the military. Can you walk us through why you decided to go to McKinsey from there?

Yeah, so the biggest thing was that I had a great academic education, but I was beginning to feel that there wasnt as much real-world experience mixed in with it. That in particular, I was eager to do as many things as I could, touching as many fields as I could, and to understand business in particular, about how people and money and goods move around the world and how that works.

KK: So you didnt just want to make a lot of money?

Whats that?

KK: You didnt just want to make a lot of money?

I definitely noticed the paycheck and that was important, too. I needed to make a living. Yeah. Im not going to pretend that that wasnt on my mind, too.

Binyamin Appelbaum: Wed like to talk about some of those real-world experiences. So one of the companies you worked for, Blue Cross Blue Shield of Michigan, you said that you were analyzing costs there, and after you completed that project, the company moved ahead with hundreds of layoffs and rate increases. Did you understand that what you were doing as a McKinsey consultant at that company that you were working to prepare for layoffs and price increases?

I had nothing to do with premiums, prices, fees or anything like that. Mostly what my team was looking at was overhead. Theres no way to know the relationship between analysis I did in 2007 and decisions they made in 2009, but certainly our focus was making sure that cost was under control there.

This video excerpt has been edited by The Weekly.

BA: You surely understood why a company like that would hire McKinsey to come in. Yeah?

How do you mean?

BA: When companies hire consultants, theyre usually trying to reduce their costs, right?

I think thats the only cost-cutting study I did out of all my time at McKinsey, so Im not sure its accurate to say that thats what most consulting work is.

BA: So it surprised you when that resulted in layoffs and price increases cases. That didnt seem like what you wouldve done if you had had that information.

I wasnt following news out of Michigan in 2009, so I found that out since, but yeah, Im not surprised. I mean, if an organization needs to cut costs, then that can involve layoffs.

BA: Another of your clients, Loblaws, the grocery chain, has since said that it was involved in the price fixing of bread during the time that you were analyzing grocery prices for them. Im curious first, just, did you analyze the price of bread for them? Is that part of your agreement?

Not in any detail. Basically the way my job worked was, they have about 50,000 items that they sold and I was creating and then crunching a database. What we would do is we would figure out, based on a years worth of sales, if they tried to cut a certain percent off their prices across a certain number of hundreds of stores, what would the impact of that be? So, bread was probably one of the U.P.C. codes in there, but I didnt pay attention to one product over another.

BA: When you were working at McKinsey, did you understand the companys purpose to be exclusively maximizing its own profitability? Did you understand the purpose of the companies you worked for to be exclusively to maximize their profitability?

Well, many of my clients as, you know, were public sector and nonprofits, so obviously their function is not about profitability. But yes, I worked for a company, a for-profit company.

BA: Do you think that that should be the sole mission of a corporation, though, to maximize profitability?

Well, I think that theres something to be said for the dialogue thats happening with, for example, whats going on in the Business Roundtable, but also this is where policy needs to come in. We cant expect corporate America to spontaneously change what it is about, without imposing different kinds of left and right boundaries.

To me, where the public sector and the function of regulation meets what private companies do is precisely to set up those kinds of boundaries. I welcome any time a company undertakes what is called corporate social responsibility, charitable activity or other factors in what they care about. I have been very interested to see the development of things like a B Corps, which has been a big conversation, especially around South Bend actually. Because one of the pioneering ones was a company called Better World Books that grew kind of up and around Notre Dame. But I also dont think we should be nave about how corporations behave unless they are regulated to ensure that their profit-seeking activities dont cause harm.

KK: In your view, if a company engages in criminal conduct, are the employees responsible for that conduct?

Well, obviously theres a whole theory in law about how liability works, but yeah, if somebody undertakes illegal behavior, they are as a general rule liable and should be.

BA: But bring that down to the practical level then: If youre working for a consultant to a company thats engaged in a massive price-fixing scheme, whats your responsibility?

Well, if you have anything to do with any wrongdoing, then youre responsible.

BA: You have criticized some of McKinseys more recent engagements with clients. Do you think that something fundamental has changed about the company since you left?

Its difficult from the outside looking in to gauge whether this reflects some kind of systemic shift or whether they just have a failure in terms of their guardrails. When I was there, there was a lot of talk about values. Firm values. Now, a lot of that was around impact and making sure that you put the clients interest first. Theres one story that they were proud of that I remember was part of our training. Where they had gotten some big contract to help a large multinational move into China, and it was going to lead to tons of work. But in the initial analysis, while they were doing their first round of work, the conclusion they reached was that this company shouldnt go to China at all. So, the story, at least the story as it was told within the firm, was that they gave the right advice, even though it cost them, right? So, you would hear a lot about a certain kind of ethic, but it was always about putting the clients interest first.

What you didnt hear as much was about whether what the client was doing had moral consequences that the firm didnt want to touch. I believe I remember a decision not to serve tobacco had been made by the time I was there. But my point is, there seems to be a problem there with assessing what they want to be associated with. Definitely with the ICE work, with the Saudi work, where you just say, this is a company thats good at helping clients meet objectives. But some of those objectives are not something we want anything to do with, and I think they need to step back and reassess what kind of client work they should take on in the first place.

KK: So you have portrayed a lot of the work that you did for McKinsey, like many analysts and junior staffers starting out in consulting, as mainly crunching data and making PowerPoint presentations and shuffling paper, more or less. Of course, there are also junior consultants and contractors who go to do government work, like Edward Snowden and Reality Winner, who see something that they think is wrong and decide to speak up. Can you tell us your opinion of Mr. Snowden and Ms. Winners actions?

Well, I think that we ought to have whistle-blower protections so that folks like that are not forced to choose between maintaining classified information and speaking up about wrongdoing. It may well be the case that were seeing the whistle-blower concept work in the way in which the current Ukraine process and investigation came about.

KK: So you think of Edward Snowden as a whistle-blower?

Not necessarily. I think he could have been, if that framework existed. Instead I think of him as somebody who divulged classified information.

KK: O.K. By some estimates, the federal governments work force is between 40 and 70 percent made up of contractors. What do you think of that ratio? What should it be ideally?

I think itd be arbitrary to just say theres some number that should be contractors. What I think we need to do, across our economy, and in some ways the federal government reflects this, is remove some of the magic between being an employee and being a contractor. So I think the biggest example were seeing of this in the new economy is, of course, with the gig economy, right?

This idea that you can drive for Uber and somehow not be a worker because you are contractor. A lot of this is about getting around labor standards. A lot of this is about cost-saving. Now, if we had a benefit structure in this country that was not only portable but also prorated, then we would be able to remove some of the magic that creates an incentive to have people be contractors rather than employees, and some of the incentives to be a part-time employer versus a full-time employer as well, for people who are employees on the books.

There will always be times, certainly in my administration, thereve been times when Ive turned, in particular, to law firms to supplement the work that our in-house legal team could do and other consultants with specialized expertise or some area where it just made more sense. Of course thats the case in the federal government too. But if its just a way to get around the obligations of having an employee, then I think it needs to be reassessed and the more that can be brought in house, the better. I guess what Im saying is we can make some changes in our economy and our benefits systems that would reduce some of the pressure to do that in the first place.

KK: This is just a yes or no question, but would you advise a senior at Harvard today to go to work at McKinsey?

Depends on the senior. I mean I get questions from people who are thinking about joining the military, as well as consulting companies, as well as political campaigns. Ill tell you when I was a senior at Harvard, they came around then, too. The standard that I had for myself was, your early 20s are such a precious time that you should prioritize what youre going to get out of your experience, way more than anything a paycheck can offer you in your early 20s and, for me, it didnt meet that standard when I was leaving college.

Pete Buttigieg speaking to a full house at New England College in Henniker, N.H. David Degner for The New York Times

KK: O.K. Were going to pivot to a new topic if you dont mind.

Mara Gay: Mr. Mayor, can you explain the mistakes that were made around your Douglass Plan? Why did your campaign falsely claim support from black leaders and then use tokenizing stock photos? Can you just talk about how that happened?

My understanding is that no false statement has ever been made about somebodys support for the plan. My understanding is that there were miscommunications about the public rollout of peoples names, all of whom had indicated at some point support for the plan, but not all of whom had reconfirmed that they were up for

MG: Right. They called it misleading.

having their names attached to that. So that was a process mistake, obviously, that led to changes in how we communicate with supporters and people that were in dialogue with about our policies. I dont know as much about the stock photo. I think it was on the website until September. I know that the vendor who was involved in running that part of the website or adding that kind of imagery has not been with the campaign for a while and obviously that was a mistake.

MG: How can you win the Democratic nomination, let alone the presidency, without the support of black voters? What do you make of the lack of support for your campaign from that community so far?

Well, I believe, first of all, that were earning support from black voters. I became mayor and was re-elected as mayor, largely because of support from every constituency, including the black community in my city. I believe that it is

Brent Staples: Whats the percentage of black citizenship there?

About 25 percent. I carried every district, including the minority-majority districts in our city, in primaries and generals, both times. I believe that anyone who proposes to be the president ought to be a president for everybody and also in particular, given what African-Americans are up against in the United States today, that the message of the Democratic Party needs to be one that speaks to black voters where they are. Its one of the reasons were being very intentional about that.

Now, I dont want to plunge in on polling numbers, but the last couple of rounds that came back suggested that the way that Im viewed among black voters is roughly the same in terms of the proportions as among white voters. But far more black voters say they dont know me or dont have an opinion. I think part of this reflects the fact, certainly something I hear from a lot of black voters, that folks feel not only abused by the Republican Party but often taken for granted by the Democratic Party. So the trust that you can build through quantity of time, through longevity, is very important. I dont have the kind of longevity that obviously some of my competitors

MG: So how do you overcome that?

So two things. First of all, the substance of what we have to offer. Im really proud of whats in the Douglass Plan. Its praised as the most comprehensive plan on dealing with systemic inequality put forward by a presidential candidate. Not, of course, because I sat in a room and thought up all these brilliant ideas, but because we had a lot of conversation and a lot of dialogue and fit our values to a plan to move forward. The more I communicate that plan, the better received it is and the better received I am.

But I also think before a lot of folks care whats in your plan, they need to know whats in your heart. And Im working in not just traditional campaign formats big speeches and TV appearances but also weve been doing more and more quiet and smaller engagements.

Our recent tour to the South, for example, had a lot of conversations that were between 20 and 50 people. Some of them very targeted around a policy issue like health equity or minority entrepreneurship. Some of it more about making sure that I was speaking to and hearing from folks who had been overlooked. So when we were in South Carolina, for example, we were with an almost all-black Democratic group in Allendale County. This is early presidential primary state, right? They hadnt seen a presidential candidate in more than a decade, and you could feel the extent to which they felt overlooked. Those kinds of engagements I think are very important, too. Its not just about obviously, our goal to win, its about deserving to win. I think that kind of dialogue coupled with all of the things that you do in traditional campaigning is really important right now.

MG: Your plans for tackling income inequality are not quite as detailed as some of the other candidates. For example, your policies on an inclusive economy say somewhat vaguely that youre going to knock down unfair barriers to entrepreneurship. What would that look like?

Sure. So first of all, we know that there are challenges to access to credit. In fact, virtually every small African-American-owned business that Ive visited in this campaign, I ask, howd you get started? Howd you get your start-up money? They always say they had to come up with the cash. Thats a pattern of course thats borne out on everything from how mom-and-pop businesses experience commercial banking to the well-documented fact of V.C. [venture capital] money, almost all going to a small handful of people and kinds of people in a certain number of places.

So there are things we can do about that. One thing we can do is capitalize CDFIs better Community Development Financial Institutions that have a much better track record of in turn supporting minority entrepreneurship. The way I would do it would be a 5X C.R.A. super credit for any of the larger institutions to flow funds into CDFIs.

Another thing we can do is direct co-investment this is part of our Walker-Lewis Initiative in businesses led by those who are underrepresented. Theres precedent for this with TEDCO in Maryland, and I think that kind of co-investment could be very powerful. Weve seen it in other countries you actually see it in the Israeli start-up community with state-supported grants.

Part of it is looking at other things that need to be reformed in credit scoring and credit systems generally, and then part of it is a little deeper in the chain of cause and effect, right? Where we know how much of the wealth in this country is inherited, not just among the ultrawealthy but just in general.

KK: Sure.

And how that flows through the implications for homeownership and access to education and health and all the other things that become barriers to folks being able to be empowered economically as they grow up.

KK: Who do you consider to be your most important advisers within the African-American communities, but also communities of color in general?

Well, first of all, our campaign team, we were about overall, I think were about 40 percent people of color.

I will turn to anybody from the local organizer in a given county that were traveling to in South Carolina to senior figures like Brandon Neal, our senior adviser on the campaign whos got a great track record from the Obama White House and the N.A.A.C.P. Or folks like our national investment chair, Swati Mylavarapu, who can speak a lot to some of those capital-formation issues. We try to make sure that Im listening to everybody I can learn from. I dont always start by getting permission for whether I can name check them, but a lot of conversation going on.

MG: Sorry. Just real quick, have you been to the museum in Montgomery?

I have. Yeah. Very recently, and it is haunting because it evokes things that Ive seen in places like Cambodia, and its on American soil. The way theyve constructed it is, I think, it forces you to understand the relationship between past, present and future. Thats, of course, all the brilliant work that Bryan Stevenson and the Equal Justice Initiative are doing. The fact that it arose out of activism on the death penalty, for example, in Alabama, a state that does not offer counsel past trial and, I think, maybe first appeal for the indigent even on death row, shows you that this is not just about marking something that happened. This is about connecting all of the patterns of injustice and surfacing the violent nature of that injustice in a way that forces us to contend with how its all connected.

BS: The death penalty as we know it evolved out of lynching.

Yes, as we know it, for sure. Which is, by the way, part of why Im calling for a constitutional amendment to end the death penalty. Anyway, it was a very powerful experience, and I think its very important for us to view not as an antiquarian kind of thing, but as a touchstone for what weve got to deal with right now.

MG: Thanks.

Aisha Harris: Mr. Mayor, you recently said that the failures of the old normal help explain how we got to Trump. Where does Obama fit into all of that? Because he was in office for eight years. I know you were misquoted at one point on that part.

You noticed.

AH: Yes, but Obama was in office for eight years. So where does he fit into the old normal as you see it?

Well, first of all, lets acknowledge that under President Obama, the Great Depression was avoided. Osama bin Laden was brought to justice. Health care was extended to millions of Americans. The auto industry was, was rescued in our country, is pretty good for eight years work. I also think that

BS: Thats the other thing that sorry to interrupt you. The other thing to that is the number of racist hate groups kind of quintupled under his leadership. I mean the mere fact of a black person in the White House brought that about.

Which is why we cant treat the Trump phenomenon as a blip or an anomaly. I mean this is surfacing things that as in a different way, the arrival of the first African-American president surfaced things that of course, had been here all along.

Were going to have to reckon with the extent to which Trump and Trumpism reflect a lot more about America than we might want to admit. Now, he was also, I think, capitalizing on a wave of populism that was responsive to what I would call a 40-year-long Reagan era that President Obama was the last Democratic president serving within. In other words, he was constrained by an atmosphere, a neoliberal consensus, where even for Democrats, most of the time, the only thing you could ever say you were going to do to a tax was cut it. There was this set of constraints that has dominated our political conversation leading to the conflagration that is Trump and Trumpism, and weve got to find our way out of it to something new.

AH: So how do you plan to sort of dismantle that old regime? Because in part, one of the issues that I think a lot of especially young people have is that you dont seem nearly as progressive or as revolutionary in some ways as some of the other candidates. Thats something a lot of young people are looking for. So how do you can you explain in a little bit more detail how you think about that?

Yeah. Sure. First of all, what Im proposing would make me the most progressive president in the lifetimes, not only of young people, but I mean, certainly in the last half century. Ill also say that it matters that we hold together an American majority that is progressive enough that it unlocks possibilities that were not available even 10 years ago during the Obama presidency. So it took everything that the Democratic Party had just to push through a health care reform in the A.C.A., invented by conservatives. Right? And that was a major achievement.

But that was as far as you could get during the constraints of that time. Where we are right now is that there is a powerfully large, not everybody obviously, but a powerfully large American majority. Not only to do the right thing on areas where Democrats have generally been trusted wages, labor, health but also areas where weve been on defense, like immigration, guns.

Holding that majority together is a big part of the task of the next president. Im not just talking about how to win an election. Im talking about how to govern this country. We need to have enough clarity of vision that we can see that the boldness of an idea is not measured only by how many people it can alienate, but by what it can get done. So theres always a more extreme solution on offer that sometimes Ill be competing with. But I also want to be very clear that what Im talking about would make the next era what Im proposing we do would make the next era very different from the one weve been living.

AH: Well, one

Thats my concern is to make that happen.

AH: So one final question. How do you convey that to younger voters? How do you counter the Mayo Pete memes? Are you familiar?

Im not. Do I want to know?

BS: You havent heard that expression?

AH: Well, mayonnaise as I think, and a lot of people think is really, really gross and there have been teens

BS: Wait a minute. [LAUGHTER]

AH: Lets not get off track.

BS: Wait a minute!

AH: Anyway, people feel strongly about mayo. There have been younger people theres a meme going around called Mayo Pete, and that I think does speak a little bit to the lack of youth support that you currently hold, even compared to those who are significantly older.

KK: A more generous interpretation is its bland.

PB: O.K.

John Broder: White.

Several others: And white. [LAUGHTER]

I get the white part.

AH: I didnt mean to imply that youre gross. [LAUGHTER] Thats not what I meant.

Well, first of all again, try to get folks to look at how big these ideas are. I mean Im talking to them about the biggest reform in the American health care system weve had since Medicare was invented. Im talking about a game-changing transformation on the availability of funds to go to college. Im talking about getting our climate carbon neutral by 2050.

That will test the limits of human capacity, and there will always be some folks who say, its not real. Health care reform isnt real unless you obliterate the entire private industry. College isnt real unless even the child of a billionaire can go without paying a penny in tuition. The climate change thing doesnt count unless its trillions more dollars than it is, and thats just not how I measured the bigness of an idea.

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Opinion | Pete Buttigieg Says He's More Than a Resume - The New York Times

The Role of Data Processing Organizations in Artificial Intelligence – Toolbox

As the use of personal computers (PCs) became more and more widespread and now the proliferation of cloud and smart devices, numerous battles over turf broke out. These involved such issues as:

1. Which part of the organization controls the selection and acquisition of these devices?2. What procedures must be followed to control access to and the modification of corporate data bases?3. How should these devices and their software be networked together?4. Who is responsible for developing or acquiring new software?

Data processing and management information system (MIS) groups have found it necessary to modify some of their established procedures to deal with the challenges of PC technology. The intent of this modification is to support distributed processing on a network of small computers while retaining the overall responsibility for ensuring that the organization's corporate resources are used most effectively. As Al technology is more widely used, what will be the change in the role of these data processing and MIS groups? Will AI become just another part of data processing?

Numerous trade-offs are possible for assigning responsibilities for developing or utilizing Al systems. Should the existing MIS group supervise the development of information systems, or should a new in-house Al group take over that responsibility? Factors to be considered include:

1. The level of interaction needed between these systems and existing corporate data bases2. Familiarity with the organization's needs, procedures and existing data-processing systems3. Cost of equipping, training, and motivating a specialized Al staff4. Built-in NIH biases ("That's not our idea, just do it the same way we always have.")5. Attitudes towards working closely with "nonprofessional" or "hands-on" experts such as those on the factory floor or in customer service6. Requirement for new specialties7. Distinctions between development of systems intended to improve internal operations and development of new products or services8. The amount of EDP resources required to develop or run an AI application program

The IT groups certainly have had extensive experience in interfacing with many elements in the organization. However, they have not always been successful in completely understanding the needs of users or the methods used in accomplishing specific tasks. Although they may be familiar with computer technology, some MIS personnel are not suited for the level of innovative development required with the current state of artificial intelligence art. Conversely, they may have become by reason of previous experience much more realistic about scheduling and cost requirements. Finally, motivations and priorities may favour the establishment of a specialized AI group.

One person spent several hours with the members of a consulting group that specialized in the design of large database systems. The purpose of the meeting was to explore the commonalities and differences between AI and "conventional" data-base system practice. There were two interesting conclusions from the meeting: First, that the Al community was just beginning to learn what the data-processing community had learned long ago, and second, that the major difference was one of focus. The designer of a data-base system must ruthlessly focus on commonality, suppressing any individual differences. The designer of an Al system, on the other hand, gives the greatest emphasis on the individual and his or her needs.

As distributed computing power becomes more ubiquitous, it may be possible to embed individual support systems within the common whole. But there is also an opportunity for building distributed support systems that span the globe much more easily and can concentrate its support to areas of need when and where the need occurs

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The Role of Data Processing Organizations in Artificial Intelligence - Toolbox

Creative storytelling with subtitles: Is artificial intelligence up for the task – ETBrandEquity.com

By Jyothi NayakTo err is human but just how true is this in the case of subtitling and captioning?

Recently a friend of mine asked me why we dont use automatic subtitling tools. Little did she know how excited I was when I heard about these tools a couple of years ago! After all, wouldnt it be wonderful to get machines to do all the hard work while we humans multi-task?

Lets take a step back and use a real-life scenario to analyze this. Platforms like YouTube have for long offered automatic captions for videos, but they are notorious for delivering sentences studded with nonsensical or occasionally obscene phrases. For hearing impaired viewers, however, this is no laughing matter, as they often depend on subtitles to decipher spoken words within a video. To address this issue, social media campaigns like #NoMoreCRAPtions have emerged which focus on ditching automatic captions.

This article is all about how subtitling is becoming increasingly relevant today, why its imperative, and what role technology can play in the evolving industry landscape. A recent study in the UK showed that more than 63% of Gen Z, who are digital natives, end up using subtitles as it not only helps them watch content on-the-move, but also aids in better comprehension.

Recent experiments in India and a few other developing countries have proved that Same Language Subtitles (SLS) have improved reading literacy. SLS causes automatic, inescapable reading engagement even among weak readers, and over a period of time has a bigger impact than conventional print media. Even developed countries plan to make SLS a default option for childrens content, in order to help young viewers develop reading skills in their early years.

As the boom in the subtitling industry fuels new business opportunities, large volumes and tight deadlines are making content creators look towards AI-based solutions. Like most other industries, AI has penetrated the translation and localization space and unlocked exciting possibilities. Today, there are several AI-based solutions that not only understand spoken words and convert them to text, but also translate them to a target language.

But the million-dollar question is are these machine-generated results as good as human translation? No, not yet! While AI can assist in the overall process of subtitling, actual translation by humans is far more impactful for local audiences, as such translation is creatively generated by native speakers of that language.

AI tools, in my view, still have several limitations. When working on genres like mythology or content with considerable background noise, heavy accents and high context content (like sarcasm or humor), the use of AI tools becomes challenging and the results are hard to work with. Within text translations as well, complex sentences can result in gibberish. For example, when translating from Hindi to English, an experienced translator would translate the reference of romantic Indian duo Laila Majnu to Romeo and Juliet something a machine would be able to do only after considerable learning. Creativity plays an intrinsic part in translating content and generating impactful subtitles.

When it comes to subtitling, the context is as important as the content. While words like mom/mother can be used interchangeably, the usage of mother is more appropriate in the context of a religious mention, which the machine will not be able to decipher automatically. Similarly, there are many common idioms and culture sensitive languages (Arabic for instance) which, when translated literally, yield hilarious and sometimes offensive results! AI tools tend to struggle with unclear contexts, new slangs and specialized subjects that require a lot of research.

So, does it mean the world of subtitling will remain human-driven even with the advent of AI? It certainly will not, as machines start learning the nuances and growing intelligence. There are many areas where automation can help reduce manual effort and increase speed right away. Examples include time-code shifting, workflows for Quality Check (QC) and auto check for compliance issues (usage of restricted words etc.) which can creep in through human errors. The good news is that theres no need to follow an all-or-nothing approach. You can choose a hybrid workflow where machine transcription takes place first, and QC is performed on this output by native translators, who correct all mistakes (and dont just laugh at them!). These corrections should ideally be fed back to the machine, so that it continues learning and eventually generates better quality subtitles. It also helps to use advanced, end-to-end AI tools that not only create transcripts, but also sync these to the prescribed number of words per second/minute, as well as to the shot boundary. Such tools deliver subtitles that are far more accurate.

Another factor to consider is that since most off-the-shelf subtitling tools have several limitations, vendors who deal in large volumes can look at building their own machine learning tools that are trained with past data to fit a particular genre/style of subtitling. This can help you generate high quality results, suited to your specific needs. Alternately, you could even consider using specialized AI tools which go a step further by using the output of multiple best-in-class engines and smartly extract the best from all of these to deliver better results.

As you can see, there is a lot of potential for automating the subtitling process, its just not completely foolproof yet! For now, leveraging an optimal mix of human talent and cutting-edge technology seems to be the best answer. AI-led automation augmented with the creativity of native speakers is the best way to meet the need for speed and volume that the subtitling industry demands today. Getting this blend right is the key for delivering multi-platform, multi-language content to worldwide audiences and increasing global market share.

-The author is SVP global localization, Prime Focus Technologies. Views expressed are personal.

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Creative storytelling with subtitles: Is artificial intelligence up for the task - ETBrandEquity.com

Clearview AI: The company that might end privacy as we know it – ETtech.com

You take a picture of a person, upload it and get to see public photos of that person along with links to where those photos appeared. By Kashmir Hill

Until recently, Hoan Ton-Thats greatest hit was an app that let people put Donald Trumps distinctive yellow hair on their own photos.

Then Ton-That did something momentous: He invented a tool that could end your ability to walk down the street anonymously and provided it to hundreds of law enforcement agencies.

His tiny company, Clearview AI, devised a groundbreaking facial recognition app. You take a picture of a person, upload it and get to see public photos of that person along with links to where those photos appeared.

Federal and state law enforcement officers said that while they had only limited knowledge of how Clearview works and who is behind it, they had used its app to help solve shoplifting, identity theft, credit card fraud, murder and child sexual exploitation cases.

Until now, technology that readily identifies everyone based on their faces has been taboo because of its radical erosion of privacy.

But without public scrutiny, more than 600 law enforcement agencies have started using Clearview in the past year, according to the company, which declined to provide a list. The computer code underlying its app, analyzed by The New York Times, includes programming language to pair it with augmented reality glasses; users would potentially be able to identify every person they saw.

Clearview has also licensed the app to at least a handful of companies for security purposes.

The weaponization possibilities of this are endless, said Eric Goldman, co-director of the High Tech Law Institute at Santa Clara University. Imagine a rogue law enforcement officer who wants to stalk potential romantic partners, or a foreign government using this to dig up secrets about people to blackmail them or throw them in jail.

Clearview has shrouded itself in secrecy, avoiding debate about its boundary-pushing technology. When I began looking into the company in November, its website was a bare page showing a nonexistent Manhattan address as its place of business. The companys one employee listed on LinkedIn, a sales manager named John Good, turned out to be Ton-That, using a fake name. For a month, people affiliated with the company would not return my emails or phone calls.

While the company was dodging me, it was also monitoring me. At my request, a number of police officers had run my photo through the Clearview app. They soon received phone calls from company representatives asking if they were talking to the media a sign that Clearview has the ability and, in this case, the appetite to monitor whom law enforcement is searching for.

Facial recognition technology has always been controversial. Clearviews app carries extra risks because law enforcement agencies are uploading sensitive photos to the servers of a company whose ability to protect its data is untested.

The company eventually started answering my questions, saying that its earlier silence was typical of an early-stage startup in stealth mode. Ton-That acknowledged designing a prototype for use with augmented reality glasses but said the company had no plans to release it. And he said my photo had rung alarm bells because the app flags possible anomalous search behavior in order to prevent users from conducting what it deemed inappropriate searches.

In addition to Ton-That, Clearview was founded by Richard Schwartz who was an aide to Rudy Giuliani when he was mayor of New York and backed financially by Peter Thiel, a venture capitalist behind Facebook and Palantir.

Another early investor is a small firm called Kirenaga Partners. Its founder, David Scalzo, dismissed concerns about Clearview making the internet searchable by face, saying its a valuable crime-solving tool.

Ive come to the conclusion that because information constantly increases, theres never going to be privacy, Scalzo said. Laws have to determine whats legal, but you cant ban technology.

Addicted to AITon-That, 31, grew up a long way from Silicon Valley, in his native Australia. In 2007, he dropped out of college and moved to San Francisco. The iPhone had just arrived, and his goal was to get in early on what he expected would be a vibrant market for social media apps.

In 2015, he spun up Trump Hair, which added Trumps distinctive coif to people in a photo, and a photo-sharing program. Both fizzled.

Ton-That moved to New York in 2016. He started reading academic papers on artificial intelligence, image recognition and machine learning.

Schwartz and Ton-That met in 2016 at a book event at the Manhattan Institute, a conservative think tank. Schwartz, now 61, had amassed an impressive Rolodex working for Giuliani in the 1990s. The two soon decided to go into the facial recognition business together: Ton-That would build the app, and Schwartz would use his contacts to drum up commercial interest.

Police departments have had access to facial recognition tools for almost 20 years, but they have historically been limited to searching government-provided images, such as mug shots and drivers license photos.

Ton-That wanted to go way beyond that. He began in 2016 by recruiting a couple of engineers. One helped design a program that can automatically collect images of peoples faces from across the internet, such as employment sites and social networks. Representatives of those companies said their policies prohibit such scraping.

Another engineer was hired to perfect a facial recognition algorithm that was derived from academic papers. The result: a system that uses what Ton-That described as a state-of-the-art neural net to convert all the images into mathematical formulas, or vectors, based on facial geometry like how far apart a persons eyes are.

Clearview created a vast directory that clustered all the photos with similar vectors into neighborhoods. When a user uploads a photo of a face into Clearviews system, it converts the face into a vector and then shows all the scraped photos stored in that vectors neighborhood along with the links to the sites from which those images came.

Clearview remains tiny, having raised $7 million from investors, according to Pitchbook, a website that tracks investments in startups. The company declined to confirm the amount.

Going Viral With Law EnforcementIn February, the Indiana State Police started experimenting with Clearview. They solved a case within 20 minutes of using the app. Two men had gotten into a fight in a park, and it ended when one shot the other in the stomach. A bystander recorded the crime on a phone, so police had a still of the gunmans face to run through Clearviews app.

They immediately got a match: The man appeared in a video that someone had posted on social media, and his name was included in a caption on the video. He did not have a drivers license and hadnt been arrested as an adult, so he wasnt in government databases, said Chuck Cohen, an Indiana State Police captain at the time.

The man was arrested and charged; Cohen said he probably wouldnt have been identified without the ability to search social media for his face. The Indiana State Police became Clearviews first paying customer, according to the company. (Police declined to comment beyond saying that they tested Clearviews app.)

The companys most effective sales technique was offering 30-day free trials to officers. Ton-That finally had his viral hit.

Federal law enforcement, including the FBI and the Department of Homeland Security, are trying it, as are Canadian law enforcement authorities, according to the company and government officials.

Ton-That said the tool does not always work. Most of the photos in Clearviews database are taken at eye level. Much of the material that police upload is from surveillance cameras mounted on ceilings or high on walls.

Despite that, the company said, its tool finds matches up to 75% of the time.

One reason that Clearview is catching on is that its service is unique. Thats because Facebook and other social media sites prohibit people from scraping users images; Clearview is violating the sites terms of service.

Some law enforcement officials said they didnt realize the photos they uploaded were being sent to and stored on Clearviews servers. Clearview tries to preempt concerns with an FAQ document given to would-be clients that says its customer support employees wont look at the photos that police upload.

Clearview also hired Paul Clement, a U.S. solicitor general under President George W. Bush, to assuage concerns about the apps legality.

In an August memo that Clearview provided to potential customers, including the Atlanta Police Department and the Pinellas County Sheriffs Office in Florida, Clement said law enforcement agencies do not violate the federal Constitution or relevant existing state biometric and privacy laws when using Clearview for its intended purpose.

Clement, now a partner at Kirkland & Ellis, wrote that authorities dont have to tell defendants that they were identified via Clearview as long as it isnt the sole basis for getting a warrant to arrest them. Clement did not respond to multiple requests for comment.

The memo appeared to be effective; the Atlanta police and Pinellas County Sheriffs Office soon started using Clearview.

Woodrow Hartzog, a professor of law and computer science at Northeastern University in Boston, sees Clearview as the latest proof that facial recognition should be banned in the United States.

Weve relied on industry efforts to self-police and not embrace such a risky technology, but now those dams are breaking because there is so much money on the table, Hartzog said. I dont see a future where we harness the benefits of face recognition technology without the crippling abuse of the surveillance that comes with it. The only way to stop it is to ban it.

Excerpt from:
Clearview AI: The company that might end privacy as we know it - ETtech.com