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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M'sian courts to go digital and adopt artificial intelligence initiatives - The Star Online

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.

Continued here:
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.

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

Assange denied access to lawyers in UK – Daily Times

Wikileaks founder Julian Assange has been denied access to evidence and even basic items like paper and pens by British prison officials, putting his US extradition case on the brink of judicial review, his lawyer has warned.

Solicitor Gareth Pierce was shocked to learn that District Judge Vanessa Baraitser only intended to allow the defence team one hour to review evidence with the Australian in the holding cells at the Westminster Magistrates Court on Monday.

Hes been charged in the US with 17 counts of spying and one count of computer hacking after WikiLeaks allegedly tried to help US army intelligence analyst Chelsea Manning conceal her virtual identity in the release of thousands of classified Pentagon files regarding the Iraq and Afghanistan wars.

Assanges supporters in the public gallery, including British rapper M.I.A., silently waved and raised fists to him and he smiled and nodded before giving them a two-fingered salute.

Ms Pierce, who had expected to have a full day with Assange, explained that her team had previously been allowed just two hours to review evidence with him in prison.

It set us back in our timetable enormously, she told the court.

We will do our best but this slippage in the timetable is extremely worrying.

Ms Pierce described how the administration of Belmarsh prison, where hes being held, had obstructed access to her client to the point where she had even had to approach UK government lawyers to assist.

She warned that further denying Assange his human right to legal access was putting his case on the brink of a judicial review.

Judge Baraitser adjourned the case until later on Monday afternoon to allow the defence team to review case evidence with Assange.

Ms Pierce, who had expected to have a full day with Assange, explained that her team had previously been allowed just two hours to review evidence with him in prison.

It set us back in our timetable enormously, she told the court.

We will do our best but this slippage in the timetable is extremely worrying.

Ms Pierce described how the administration of Belmarsh prison, where hes being held, had obstructed access to her client to the point where she had even had to approach UK government lawyers to assist.

She warned that further denying Assange his human right to legal access was putting his case on the brink of a judicial review.

Judge Baraitser adjourned the case until later on Monday afternoon to allow the defence team to review case evidence with Assange.

In that second sitting, Ms Peirce said that she had only had an hour to speak to Assange.

Wikileaks ambassador Joseph Farrell called Assanges severely limited access legal representation to date as outrageous.

Given the way Belmarsh is dealing with this, its on the brink of judicial review, he told AAP.

To have three hours with your lawyers when youre facing 175 years in prison (in the US) is not acceptable.

Academy and Grammy award-nominated hip-hop artist M.I.A., who visited Assange in prison last year, said authorities had even denied him simple things like a pen and paper.

She said some books were denied as well due to concerns he could use them to secretly communicate with outsiders.

It blows my mind that England can have this going, and with the support of Australia, M.I.A. told AAP.

Mr Farrell said given the number of stumbling blocks presented to Assange it raised the question of whether the biggest media freedom case this century was actually a fair trial.

More importantly than all that, the fact that this is a trial at all is outrageous, he told AAP.

This is somebody who is in prison for exposing war crimes, for doing his job. Hes in prison for the very same reason as he was given a Walkley Award. This is not something that should be allowed to happen.

Assanges next hearing is scheduled for January 23. He is due to appear via video link from Belmarsh prison.

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Assange denied access to lawyers in UK - Daily Times

Dissenter Weekly: Blowing Whistle On Business Of War In IraqPlus, Honduras and DOJ Cheat Whistleblower – Shadowproof

On this weeks Dissenter Weekly Update, host and Shadowproof editor Kevin Gosztola discusses how military contractors are speaking out after President Donald Trump assassinatedand attempted to assassinateleaders of militias aligned with General Qassim Soleimani.

Current and former employees for a military contractor called Sallyport Global Services claim the Iranian-backed militia, Kataib Al Imam Ali, allegedly stole military hardware and issued death threats against their employees. The company, which had a billion-dollar contract with the Pentagon, bribed the militia with free trucks and a first, second, and third base for their operations. These fighters were aligned with the United States, probably fighting ISIS, wasting taxpayers dollars like most military ventures. Its how the business of war works.

Later in the program, Gosztola highlights a story involving an ex-employee of a firm that was contracted by Honduras to help the country rebuild their water and sewer systems. The ex-employee filed a whistleblower lawsuit because the Honduras government refused to pay the firm $51 million, and the Justice Department is backing the Honduras government.

Gosztola provides an update on WikiLeaks founder Julian Assanges extradition case. He will travel to London in February to cover an extradition hearing for WikiLeaks founder Julian Assange that has grave implications for global press freedom. Help us fund his trip by making a donation at https://shadowproof.com/donate.

The Dissenter Weekly Update airs every Thursday at 4pm ET on YouTube and covers whistleblower and press freedom news from that week.

Regulatory Board Accused Of Proposing Corporate-Friendly Rules To Deal With Accidental Pollution

Ex-Employee Of Ruined Alabama Firm Battles Honduras, US Government In Whistleblower Case

Bloomberg Insists Female Former Employees Should Not Be Freed From NDAs

Before the U.S. Bombed Soleimanis Militia Leaders, It Bribed Them

Julian Assange Still Denied Access To Lawyers In Fight Against U.S. Extradition

Former New York Times General Counsel: Will Alleged CIA Misbehavior Set Julian Assange Free?

***

As of this recording, Chelsea Manning has been in jail for 309 days and owes $200,000 dollars in fines.

Julian Assange has been in jail for 273 days since he was expelled from the Ecuador embassy in London.

Subscribe to Shadowproof on YouTube and send tips and feedback to editor@shadowproof.com

This show is brought to you by Shadowproof.com, a 100% reader-funded press organization. If you enjoy our work, you can support us with a donation or by subscribing for $5/month or more: https://shadowproof.com/donate

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Dissenter Weekly: Blowing Whistle On Business Of War In IraqPlus, Honduras and DOJ Cheat Whistleblower - Shadowproof

Pricing – Machine Learning | Microsoft Azure

For open source development at cloud scale with a code-first experience. Basic + UI capabilities + secure and comprehensive machine learning lifecycle management for all skill levels. Automated machine learning Create and run experiments in notebooks Available Available Create and run experiments in studio web experience Not available Available Industry leading forecasting capabilities Not available Available Support for deep learning and other advanced learners Not available Available Large data support (up to 100GB) Not available Available Interpretability in UI Not available Available Machine Learning Pipelines Create, run, and publish pipelines using the Azure ML SDK Available Available Create pipeline endpoints using the Azure ML SDK Available Available Create, edit, and delete scheduled runs of pipelines using the Azure ML SDK Available Available Create and publish custom modules using the Azure ML SDK Available Available View pipeline run details in studio Available Available Create, run, visualize, and publish pipelines in Azure ML designer Not available Available Create pipeline endpoints in Azure ML designer Not available Available Create, edit, and delete scheduled runs of pipelines in Azure ML designer Not available Available Create and publish custom modules in Azure ML designer Not available Available Integrated notebooks Workspace notebook and file sharing Available Available R and Python support Available Available Notebook collaboration Available Available Compute instance Managed compute Instances for integrated Notebooks Available Available Sharing of compute instances Available Available Collaborative debugging of models Available Available Jupyter, JupyterLab, Visual Studio Code Available Available Virtual Network (VNet) support for deployment Available Available SDK Support R and Python SDK support Available Available Security Role Based Access Control (RBAC) support Available Available Virtual Network (VNet) support for training Available Available Virtual Network (VNet) support for inference Available Available Scoring endpoint authentication Available Available Compute Cross workspace capacity sharing and quotas Not available Available Data for machine learning Create, view or edit datasets and datastores from the SDK Available Available Create, view or edit datasets and datastores from the UI Available Available View, edit, or delete dataset drift monitors from the SDK Available Available View, edit, or delete dataset drift monitors from the UI Not available Available MLOps Create ML pipelines in SDK Available Available Batch inferencing Available Available Model profiling Available Available Interpretability in UI Not available Available Labeling Labeling Project Management Portal Available Available Labeler Portal Available Available Labeling using private workforce Available Available

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Pricing - Machine Learning | Microsoft Azure

Machine Learning in Human Resources Applications and …

Human resources has been slower to come to the table with machine learning and artificial intelligence than other fieldsmarketing, communications, even health care. But the value of machine learning in human resourcescan now be measured, thanks to advances in algorithms that can predict employee attrition, for example, or deep learning neural networks thatare edging toward more transparent reasoning in showing why a particular result or conclusion was made.

The value beyond numbers for CEOs and managersis the power inunderstanding whats actually happening within acompany i.e. withtheir people. AsGlintsJustin Black articulated in awebinar for the Human Capital Institute(HCI), executives and leaders need information that helps them point people in the right direction; informationsales data, KPIs, etc.change over time, and machine learning can react faster than people in helping draw out the insights and inferences that might otherwise take reams of manpower or not be uncovered at all.

Though not an exhaustive list, belowis an outline of solid examples of machine learning and artificial intelligence applications at work in human resources today, along with developing and near-future applications.

Applicant Tracking & Assessment

Applicant tracking and assessment has topped the list in early machine learning applications, especially for companies and roles that receive high volumes of applicants.Glintis not an AI company, but they use AI tools to help companies save money and provide a better work experience. Machine learning tools help HR and management personnel hirenew team members bytrackinga candidates journey throughout the interview process and helping speed up the processof getting streamlined feedback to applicants.

Peopliseis another solutionfor helpingcompanies calculate fit score for new talent, combining tools like digital screening and online interview results to help hiring managers arrive at decisions.

While competition for the best people has driven many HR departments to use algorithmic-based assessments, aCEBarticle on using machine learning to eliminate bias cautions that human oversight isstill of paramount importance. Its not enough to act directly on data insights, but to use this information in tandem with driving question such as: 1) how I can link applicant traits to business outcomes; 2) which outcomes should be our focus when hiring; and 3) can predictions (hiring and otherwise) be made in an unbiased way.

Attracting Talent

Attracting talent beforehiring has also seen an upswingin machine-learning based applications in the past few years. Black, who is Glints senior director of Organizational Development, named LinkedIn as an example of a company using one of the most common versions of basic machine learningrecommendingjobs. Other job-finding sites, including Indeed, Glassdoor, and Seek use similar algorithms to build interaction mapsbased on users data from previous searches, connections, posts, and clicks.

PhenomPeople is one example of a suite of machine learning-based toolsthat helps leadpotential talent to a companys career site through multiple social media and job search channels. Black notes that this is really just one step past a keyword search, albeit a big step computationally, as theres a lot more to do.

Attrition Detection

Understanding people and why they decide to stay at or leave a job is arguably one of the most important questions for HR to answer. Identifying attrition risk calls for advancedpattern recognition in surveying an array of variables.

In the earlier mentioned HCI webinar, Black describes a hypotheticalsituation of identifying specific risk factors based on scores to an employee survey. If a human were to try and detect attrition risk among female engineers in Palo Alto with less than 2 years of tenure, the variance analyses to reach that conclusion are innumerable, like finding a needle in haystack, but machine learning allows us to connect these dots in seconds, freeing HR representatives to spend time supporting teams instead of analyzing data.

Glints employee engagement platform

Advances in NLP have included the ability to process large amounts of unstructured data, and algorithms can also do things like identify emotional activity in comments and tease out prescriptive comments, or actionable suggestions. Black describesprototypicality algorithms that can pull out individual comments thatrepresent the sum of what everyones saying, allowing companies to get a broadly inclusive but digestible pulse on company processes and specific issues.

JPMorgan is apparently one of several financial institutions that hasalso put into place algorithms that can survey employee behavior and identify rogue employees before any criminal activity takes place, an obviously more insidious form of attrition with dire consequenceswatch the interview with Bloomberg Reporter Hugh Son as hediscussesthese new safeguards with Bloomberg Technology.

Individual Skills Management/Performance Development

Machine learning is showing its potential inboosting individual skill management and development. While there is definitely room for growth in this arena, platforms that cangive calibrated guidance without human coaches save time and provide the opportunity for more people togrow in their careers and stay engaged.Workday is just one example of a company building personalized training recommendations for employees based on a companys needs, market trends, and employee specifics.

Black elaborates that these types of performance development assessments are useful when actually read, which is why this type of machine-based feedback has been successful for individuals. But this becomes more difficult at the level of the organization, where its almost impossible to make sense of enormous amounts of varying data; this is an area wheremachine learning is evolving, with an increased focus on the overall performance of the corporate lattice.

Enterprise Management

As alluded to in the last example, enterprise management and engagement based on machine learning insights is already here in early forms but has yet to be taken to scale. KPMG promotes its customized Intelligent Enterprise Approach, leveraging predictive analytics and big data management to help companies make business decisions that optimize key KPIs and other metrics.re:Work, which provides best workplace practices and ideas from Google and other leading organizations (including KPMG), is an excellent resource for staying up-to-date on new tools and case studies in this space.

Googles People Analytics department has been a pioneer in building performance-management engines at the enterprise level. From an early stage, the team (led by Prasad Setty) posed existing questionsfor example, whats the ideal size for a given team or departmentbut focused on finding new ways to use data in order to help answer these questions. In turn, People Analytics has helped pave the way for solving fundamental business problems related to the employee life cycle, with afocus on improving Googlers'production and overall wellness. Asoutline by Chris Derose for The Atlantic, over the last half of a decade, the team has produced insights that have led to improvements in company-wide actions, such as:

Post-Hire Outcome Algorithms

CEB notes that theideal hiring algorithm would predicta post-hire outcome (for example, reducing time taking customer service calls while keeping customer satisfaction high) rather than just matching job requirements with items on an employees resume or pre-hire assessment results.

The article goes on to note that its sometimes the counterintuitive aspectsthat predict job performance, informationthat a machine is better at findingthrough analysis than human inference. For example, CEB describes a model created for a call center representative role that linked call center experience to resultingpoor performance. While a link to the source or actual model would be helpful, the idea is interesting and reflects machine learnings strengths in invisiblepattern recognition

Internal Management

WhenTalent AnalyticsChief Scientist Pasha Roberts discussed the role of predictive analytics in human resource management with Emerj in 2016, he brought up the internal movement of employees within a company as an issue unique to HR and analytics. You can use agent-based modeling to simulate and look at how people can move within a companyand be better able to hire a person at the entry-level that will be likely to move through corporate ladder, said Roberts. While there are early systems in place, more data over time should lead to a more robust and scalable model for internal management over the nextfive years.

Increased Behavior Tracking and Data-Based Decision Making

Ben Waber, president and CEO ofHumanyzeand also a past guest on Emerj, talked about the increasing use of IoT wearable data in the workplace. These types of gadgets are more common at the enterprise levelbluetooth headphones and smart ID badges, for exampleand companies are continuing toadd sensor technology to the workplace in order to collect data. This is an area that Waber researched while serving as a visiting scientist at the MIT Media Lab, using data collected from smart badges to look at things like employee dialogue, interaction, networks within a company, where people spent their time, etc. It would seem that privacy might be a concern, but technologies like smart badges are starting to proliferate quickly (with vendors like Atmel, in the below video, introducing new and updated apps for Android phones). This type of data, says Waber, allows us to pose and answer crucial business-driving questions that we couldnt ask before, such as how much does my sales team talk to my engineering team?

Things to Keep in Mind:Machine Learning in Human Resources

Google People Analytics Lead, Ian OKeefe, told a story at the People Analytics & Future of Work conference inJanuary 2016 about his teams efforts to quantify things like efficiency, effectiveness and employee experience by looking at hiring decisions, teamclimate, and personal development. In the end, his team found that people armed with better data make better decisions than algorithms alone can do.

Well-designed AI applications, says Black, have three main cross functions: main expertise, data science expertise, and design/user experience expertise. At present, very few providers do all three of these well. The best solutions today and in the near futuredont replace humans, but emphasize scaling better decision making with the use of machines as a tool and collaborator.

Our survey of machine learning in human resources illuminates the development of a more people-centric approach, paving the way for more more valuable programs and less wasted time; reduced bias in programs; less administration and more individual development; and the ability to act proactively rather than reactively, moving seamlessly fromthe level of the individual to the organization and back again.

Image credit: Corporate IT

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