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I MUST BE A RACIST BECAUSE I, TOO, SUFFER FROM “NEGRO FATIGUE”

June 19, 2020 Humor

This says it all: I have noted that black dominated and run governments fail anywhere they are tried, anywhere in the world. City, state, county, country – all failed areas that require white resources to bail them out and survive. If anyone can name a predominantly black governmental unit, with the primary resource base being black, and the governing officals being black, that is prosperous, I’d like to know about it. Anywhere in the world.

I’m suffering from a severe case – not of Covid-19, but of Negro Fatigue.

My own Great Awokening came in the wake of the Ferguson riots. Don’t get me wrong.

I’m a Midwestern man of a certain age, and I’d always known, or at least suspected, that blacks were not exactly like whites. But, going to the suburban schools that I did, the blacks that I encountered were mostly decent, well socialized types from decent families. I got along with them fine. When I went to college, there were more of the typical ghetto Negro, but the school was large enough, and they kept to themselves enough, that I didn’t have to interact with them much, if at all. Since graduation, I have had more interactions with those types, but I still maintained the philosophy that we are all equal, and if there are differences, they are cultural (nurture) rather than genetic (nature).

Then came Ferguson. You had the national outrage, the riots, and yet, within two days, we knew enough about what actually happened (giant black attacks white cop and gets killed) to know there was no reason for the rioting. And yet, they rioted. And I kept thinking, “Why won’t they listen to reason?” That started me researching, and finding blogs, sites, and authors outside the mainstream media. I found Vdare, I found Amren, and I found others – including Cold Fury. And what I found was that the work of those authors was far better researched, backed up with unassailable statistics and facts, and explained events better than anything the mainstream media was publishing. That’s when I realized that blacks are not like whites, and very well may be incompatible.

Now I know things. For instance, I know that in 2019, nine unarmed black men were killed by police. “Unarmed” being a relative term, of course. Another 236 armed men were killed by police. But 7,145 black men were killed by other black men. And yet our entire country focuses on the nine, a number that is statistically insignificant. I know that 85% of all interracial crime has a black offender. I know that, despite being only 13% of our population, blacks commit over 50% of the violent crime in the United States. I know that, just at the Federal level, blacks consume nearly $ 1 trillion more of our resources than they contribute back in taxes (which means that until C19, nearly all of our Federal debt and deficits could be laid at the door of black America). And – these are the kinds of things that, once you know them, you cannot un-know them.

I have noted that black dominated and run governments fail anywhere they are tried, anywhere in the world. City, state, county, country – all failed areas that require white resources to bail them out and survive. If anyone can name a predominantly black governmental unit, with the primary resource base being black, and the governing officals being black, that is prosperous, I’d like to know about it. Anywhere in the world.

And why is this? It boils down to IQ. Average black IQ in the United States is 85. Average white IQ is 100. What’s the average IQ needed to have a functioning first world society? Well, I don’t know the exact number, but I know it must be above 85 and less than 100.

Blacks are now again proposing “reparations for the sin of slavery” in the amount of $ 14 trillion. At first, I thought that was absurd – but in actuality, it might be a good investment with one stipulation. Repatriation. Think about it – $ 14 trillion to get rid of our black population might be the best investment, with the shortest payoff, that we’ve ever seen. The thing is that they would ALL have to go. Every last one of them, back to Africa, to build Wakanda. And they could never return. Not that we would ever have the backbone to do it – but it’s a magnificent thought.

Because, here’s the thing. Whatever we do, whatever we give them, whatever concessions we make, it will never be enough. NEVER. The riots, Black Lives Matter, etc., are only grifts to get more and more resources and gibs from whites – without whom they cannot survive. The bulk of them cannot be persuaded to change, because persuasion requires intellect, and most simply lack that intellect. The constant cry for “black outreach” by Republicans is pathetic for this reason.

I don’t “hate” black people. You can’t hate people for being what they are. If you allow a gorilla into your back yard and then the gorilla kills you, you cannot hate it for being what it is. Nor do I wish them to come to any harm. What I wish is for them to be separate from me. And we might be heading in that general direction right now – the demand for “safe spaces” and “no cops in our communities” is nothing but a demand for segregation. We just need to heed it.

In fact, I think there’s an argument to be made that one of the worst things we ever did to blacks was DEsegregation. Follow along. Before the civil rights movement, every city had a “black area,” but not necessarily a ghetto hellhole. There were prosperous black businesses that weren’t just pawnshops, weave shops, etc. They had hotels, restaurants, clothing stores, groceries, and other respectable businesses. The men who ran those were the community leaders and role models. Were those areas as nice as white areas and as prosperous? No. Not normally. But they DID provide a stable social structure and kept the lawlessness under control. The illegitimacy rate was higher than whites’ at 25%, but most kids grew up in two-parent families and had home lives. Then came desegregation.

Lefties talk about the sin of “white flight,” but they never mention that it was preceded by BLACK flight. As soon as they were able, those prosperous blacks got the hell out of the black areas and moved into white neighborhoods. Which removed the social structure in those areas (and probably lowered the aggregate IQ of those areas by a good 10 points, since the smarter ones were the prosperous ones). What was left for “role models?” Well, gold-encrusted obese “preachers,” pimps, drug dealers, and gang leaders. Thus began the descent of black America into the hellhole that it is. Now, 3 out of 4 black children are illegitimate, and you can bet that nearly all of those are in the ghettos. They run feral as their mamas run around breeding more. This has been facilitated by “feed and breed” welfare programs that encourage this behavior. The “talented tenth” left the ghetto, and the untalented nine-tenths are what’s left. What small social controls there are in the ghetto are now put there by the very police that BLM wants to get rid of. The nine-tenths are now destroying our cities nationwide for no reason other than they can and they want to fulfill their natural destructive impulses.

There’s a good case to be made that blacks, writ large, have DEvolved rather than Evolved in the last sixty years. You can see this culturally in their music. Sixty years ago, black artists sang about love and passion – now it’s killing cops and screwing ho’s. You can even see this in their crime. In the 70s and 80s, black gangs centered around highly organized gangs like the Bloods and the Crips. Now it’s just amorphous neighborhood gangs. Some – that talented tenth – have done very well from Affirmative Action, racial set-asides, college admissions preferences, and other programs. The rest – those who are really incapable of making anything of their brains – must either become athletes, entertainers, or thugs. Notice that the conversation is about “escaping the ghetto,” not improving the living conditions through better behavior. It’s been said that if you give white men a pile of bricks, they will build a city – if you give black men a city, they will turn it into a pile of bricks. Reality proves that.

So where does all this leave us? At some point, we must realize that we cannot now, nor ever, live side by side in harmony with blacks. Right now that is the most taboo of all taboos – “racism” is considered the worst sin in the world. But I wonder how many other people will be “awokened” like I was after Ferguson. I cannot tell you how many whites have said to me privately (always prefaced by “I’m not racist” of course), “Look, they are getting ‘justice for George.’ Why won’t they let it go?” The answer is simple. Because they lack the intellectual ability to do so. A couple of times I have voiced that, and those people have come back to me a few days later having done the same research I did six years ago. And arrived at similar conclusions.

The future will not be pretty, one way or another. But, ultimately, either we separate or they will destroy us.

All of which makes this generous offer sound better and better.

While tensions continue in the United States due to the ongoing problem of police brutality and racism, Ghana is reaching out to Black Americans. According to Newsweek, the tourism minister of Ghana recently encouraged Black Americans to “leave where you are not wanted.”

Barbara Oteng Gyasi extended the invite during a ceremony in Ghana honoring George Floyd, one of several Black Americans who have been killed by police in recent months.

“We continue to open our arms and invite all our brothers and sisters home. Ghana is your home. Africa is your home. We have our arms wide open ready to welcome you home,” Gyasi said during the ceremony.

Before they leave, though, they must be made to sign an ironclad, no-loopholes contract foreswearing any right to ever return. Once out, out for good. And those who stay here should sign a similar one in which they pledge to shut the fuck up for good about how awful, how inhuman, how RAYCISS!! this country is.

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ANTZ-IN-PANTZ ……

Businesses and Employees Must Prepare for W-4 Changes Coming in January 2020

November 17, 2019 NetSuite

Posted by Marc Holliday, Senior Product Marketing Manager

Changes are coming to one of the most ubiquitous tax forms, so taxpayers—and businesses— need to be prepared.

Most commonly referred to as Form W-4, the Employee’s Withholding Allowance Certificate determines the amount of Federal Income Taxes to be withheld from an employee’s paycheck. Employees fill it out when they start a new job, have a change in marital status, or some other material change in their financial situation occurs such as a spouse getting a new job or when adding dependents for birth or adoption.

The IRS encourages everyone to use the Tax Withholding Estimator to perform a quick paycheck checkup to protect against having too little tax withheld and facing an unexpected tax bill. This is even more important following the recent changes to the tax law for 2018 and beyond.

In December 2017, the Tax Cut and Jobs Act (TCJA) was signed into law and, after being delayed a year, changes to Form W-4 are going into effect for the 2020 tax year.

Let’s take a look at some of the biggest changes coming in 2020:

The elimination of withholding allowances on the form itself

Allowances are no longer used for the redesigned Form W-4 to increase transparency, simplicity and accuracy. In the past, the value of a withholding allowance was tied to the amount of the personal exemption. With the new law, you cannot claim personal exemptions or dependency exemptions. [1] Instead, allowances are represented as actual dollar amounts based on formulas found on the new form.

Changes to Employee Filing Status

Employees will still have three options to choose from in the new form, but they are slightly different. The options have gone from:

Single or married filing separately
Married filing jointly
Head of household.

To:

Single
Married
Married but withholding at a higher rate.

If no status is filled out, the default is single with no adjustments.

Impact on Employees

Employees who have filled out a W-4 in 2019 or before are not required to fill out a new W-4 in 2020.

Impact on Employers

Employers must be prepared for two scenarios for the adoption of the new form:

Employees hired after January 1, 2020 will need to fill out the new W-4
Anyone wishing to adjust their withholding after January 1, 2020 must use the new form

Check your Payroll Solution

Businesses should ask if or when their payroll solution will be offering the new form. Make sure the same withholding tables are used for either the old or new form. Since employee’s who filled out a W-4 prior to 2020 do not have to fill out another form, it is important that the payroll system correctly account for the withholding and not flag an employee as not having filled out the form.

Employers should also look for a payroll solution that implements effective dating throughout the system. It should also provide the reporting and analytics to quickly identify missing W-4s and help identify which employees are subject to the new form or are compliant using the old form.

To learn more about how NetSuite SuitePeople Payroll can help you process payroll in minutes not hours, listen to how a NetSuite customer, Advanced Piping Products, streamlined its payroll processes.

Posted on Wed, November 13, 2019
by NetSuite filed under

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The NetSuite Blog

AI Weekly: A growing chorus of experts agrees facial recognition systems must be regulated

July 19, 2019 Big Data

On Tuesday, Oakland became the third U.S. city after San Francisco and the Boston suburb of Somerville to ban facial recognition use by local government departments, including its police force. The ordinance adopted by the city council, which was written by Oakland’s Privacy Advisory Commission and sponsored by Councilmember Rebecca Kaplan, prohibits the city and its staff from obtaining, retaining, requesting, accessing, or using facial recognition technology or any information gleaned from it.

Oakland’s ban was long expected — the Oakland Privacy Advisory Committee endorsed the legislation’s wording late last month — but it comes as a growing chorus of AI experts, privacy advocates, and lawmakers express concerns over the largely unregulated tech’s applications.

A September 2018 report revealed that IBM worked with the New York City Police Department to develop a system that allowed officials to search for people by skin color, hair color, gender, age, and various facial features. Elsewhere, the FBI and U.S. Immigration and Customs Enforcement are reportedly using facial recognition software to sift through millions of driver’s license photos, often without a court order or search warrant. And this past summer, Amazon seeded Rekognition, a cloud-based image analysis technology, to law enforcement in Orlando, Florida and the Washington County, Oregon Sheriff’s Office. The City of Orlando said this week it discontinued its Rekognition pilot, citing a lack of necessary equipment or bandwidth. But Washington County used Rekognition to build an app that lets deputies run scanned photos of suspected criminals through a database of 300,000 faces, which the Washington Post claims has “supercharged” police efforts in the state.

Nonprofit advocacy group Fight for the Future yesterday published a map highlighting the speed with which facial recognition is spreading. The map lists dozens of U.S. airports, state and local law enforcement agencies, states, and cities where such systems are in active use.

Discouragingly, as experts have repeatedly noted, there appears to be little correlation between facial recognition systems’ respective accuracy and the pace of their deployment. It was recently revealed that a system used by London’s Metropolitan Police produces as many as 49 false matches for every hit. During a House oversight committee hearing on facial recognition technologies in 2017, the U.S. Federal Bureau of Investigation admitted that the algorithms it uses to identify criminal suspects are wrong about 15% of the time. And MIT Media Lab researcher and Algorithmic Justice League founder Joy Buolamwini discovered in audits of facial recognition systems — including those made by Amazon, IBM, Face++, and Microsoft — that they performed poorly on young people, women, and people with dark skin.

The evidence has led analysts like Clare Garvie, a senior associate at the Georgetown University Center on Privacy and Technology and coauthor of the Perpetual Lineup report, which monitors trends in computer-assisted facial recognition, to conclude that facial recognition technology could cause extraordinary harm. Last month, the center released reports detailing the NYPD’s use of altered images and pictures of celebrities who look like suspects to make arrests, as well as real-time systems being used in Detroit and Chicago and tested in other major U.S. cities.

“Imagine if we had a fingerprint lab drawing fingerprints or drawing a latent print’s finger ridges with a pen and submitting that to search,” she told the House Oversight and Reform Committee in May. “That would [be] a scandal, that would be a reason for a mistrial or convictions being overturned, and it’s hugely problematic.”

Perhaps unsurprisingly, beyond the adoption of outright bans, lawmakers at the national, state, and local levels have pushed back against unfettered facial recognition software. U.S. Congress House Oversight and Reform Committee hearings in May saw bipartisan support for limitations on systems use by law enforcement, and state legislatures in Massachusetts and Washington have considered imposing moratoriums on face surveillance platforms. Separately, the California State Legislature is currently weighing a facial recognition ban on police body cam footage, as is the Berkeley City Council.

But calls for regulation and cooperation with watchdog groups haven’t exactly been universal. When Georgetown researchers first requested facial recognition records from the NYPD in 2016, they were told that no such records existed — despite the fact that the technology had been in use since 2011. Only after two years in court did the agency turn over 3,700 pages of documents related to its facial recognition software use.

Garvie and others say that in future legislation, they’d like to see mandatory bias and accuracy testing, court oversight, minimum photo quality standards, and public audits (like the annual surveillance tech use reports already required in San Francisco). Critics also advocate against real-time facial recognition use by police and the practice of scanning driver’s license databases with facial recognition software. And many believe that prosecutors and police should be obligated to tell suspects and their counsel if facial recognition aided in an arrest.

It’s a lengthy checklist, but Garvie believes it’s the baseline required to deploy facial recognition technology responsibly.

“What we’re seeing today is that in the absence of regulation, [facial recognition] continues to be used, and now we have more information about just how risky it is, and just how advanced existing deployments are,” she said in a previous statement. “In light of this information, we think that there needs to be a moratorium until communities have a chance to weigh in on how they want to be policed and until there are very, very strict rules in place that guide how this technology is used.”

Thanks for reading,

Kyle Wiggers

AI Staff Writer

P.S. Please enjoy this segment from CBS about how facial recognition technology is outpacing the law.

https://www.youtube.com/watch?v=__De_2N3nWM

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Big Data – VentureBeat

Why DBAs Must Embrace DevOps

May 12, 2019 BI News and Info

A good day for a database administrator (DBA) is a lot like a good airline flight. A lot of excitement in either situation is bad. In a perfect world, a DBA will be productive and not spending most of their time “putting out fires.” (And fires are never a good idea on a flight!)

DBAs are expected to keep their systems up and running smoothly and securely. Developers, on the other hand, must deliver new features at an ever-increasing pace. Often these features require changes to infrastructure, including database changes, which may seem like introducing chaos into otherwise stable systems.

It’s obvious that developers and DBAs have conflicting goals, but both stability of systems and quickly delivering features which bring value to the customer are critical to an organisation’s success. Traditionally, developers could just write code to fulfil a set of requirements, and then let someone else, the operations side, figure out how the code should be implemented. These changes, including changes to the database, can be bottlenecks as the DBA and other operations folks ensure that the changes are safe and there is minimal disruption. There is often poor communication between teams and lots of blame to spread around when things go wrong.

One may conclude that the developers should just slow down and stop creating all this change, but companies that don’t innovate – and quickly – will be replaced by those that do. On the other hand, customers will also move on to other suppliers when services are not available due to unplanned outages.

To solve these problems, organisations are embracing DevOps methodologies to quickly bring value to customers while maintaining stability and decreasing deployment failures. DevOps is not a prescriptive set of steps to follow, nor a framework like Agile. It’s more of a culture change that begins by building communication and trust among teams and breaking down the silos between them. It also means smaller, more frequent releases of code. Instead of a couple of large deployments with lots of changes every month, some companies have many deployments each day with a small number of changes in each.

The latest State of Database DevOps survey found that 58% of the organisations participating had already adopted DevOps across some or all projects, and 27% planned to do so in the next two years. It also found that 66% did not include the database as part of the automated build and deployment process! There are often database changes, like new tables or columns, that must be deployed along with application changes, so this can be the bottleneck that slows down the delivery of new features.

With the right tools, DBAs can include databases in source control and generate deployment scripts that can be integrated smoothly with the changes from the other teams. Dev, test, QA, and staging databases that resemble production can be created in seconds, and private information can be sanitized automatically. Automating these tedious, time consuming steps that are so important for the DevOps pipeline frees up the DBA’s time so that they can focus on the things that require their expertise like stability, performance, availability, and security.

It isn’t easy to automate database changes so that they stay in sync with application changes, but there are companies doing it and doing it well. The only way to accomplish this is for DBAs to embrace DevOps.

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AI’s many challenges must be thoughtfully addressed

March 16, 2019 Big Data

In the future, personalized AI assistants will use knowledge of our behaviors to inform us of goings-on and do chores, and as we grow older, they’ll provide companionship and assistance when caregivers aren’t around. These assistants will not only chart our calendars and supply answers to questions, but will help administer medical treatments, assist us in making difficult decisions, and connect us with the people we care deeply about.

That’s the vision the Computing Community Consortium (CCC), an organization representing over 220 North American academic departments, industrial research labs, and professional societies, articulated in a draft of its 20-year roadmap for AI research in the U.S., which was published this month. Its organizers — who include University of Southern California director of knowledge technologies Yolanda Gil and Dr. Fei-Fei Li, a Stanford professor and formerly Google’s chief AI scientist — picture personal assistants that will fundamentally transform human lives around the world for the better.

“[AI] will enable an elderly population to live longer independently, AI health coaches will provide advice for lifestyle choices, [and] customized AI tutors will broaden education opportunities,” according to the report. “[A]nd AI scientific assistants will dramatically accelerate the pace of discovery.”

Eric Schmidt, former executive chairman of Google and Alphabet, and Helen Greiner, cofounder of iRobot, are in full agreement. Both shared their visions in a press gathering last month during MIT’s celebration of its new College of Computing, which was created in part to advance AI education.

“[Y]ou’ll have an [AI] assistant, [and] this assistant will express his or herself in many different ways. [A]s a child, you’ll have a teddy bear, which will help you learn language and math and what have you, and as an adult, you’ll have some other kind of assistant which will help you plan your day,” Schmidt said. “And as an elderly person, you’ll have an assistant that will keep your company [and] help you with any problems that you’re having.”

Greiner expects that concurrently, as AI becomes capable of performing more tasks, its transcension from the virtual world (e.g., smart speakers and smartphones) to the physical (driverless cars and highly dexterous robots) will accelerate.

“It’s about getting the physical into it. Think of all the time people used to waste vacuuming their floors,” she said. “[But] we’re still doing all this stuff — washing the windows, putting the dishes away — that’s the same rote types of jobs. [What we need to] envision is computation with the physical: a house that takes care of you, an automobile that drives you, and personal transport that gets you where you want to go. So many aspects of [our] life are embedded in the physical, and we need to apply that computation to the physical. I think that’s where the greatest [advances will come] in the next decade.”

There’s no doubt about it: They’re lovely predictions. But if the industry doesn’t address the issues threatening to derail progress it’s made toward them, a far grimmer reality might emerge.

The third in a series of studies by the American Automobile Association (AAA) found that nearly three-quarters of consumers are wary of riding in self-driving cars, with most predicting it will take at least a decade for the technology to mature. And in a recent Edelman survey of the U.S. general public and tech executives, about the same percentage of both groups believe AI will lead to more isolation because “there will be less need for people to interact with others” and worry that it will lessen “creativity” and “freedom of thought.”

AI also stands to disproportionately benefit the highly skilled at the expense of the economically disenfranchised. The World Economic Forum, PricewaterhouseCoopers, Gartner, and the McKinsey Global Institute predict technologies like robotic process automation (RPA) could make redundant as many as 75 million jobs by 2025 and depress the percentage of “low digital skills” from 40 percent to 30 percent in the next 10 years.

In the face of these extraordinary challenges, then, what steps should be taken to ensure the AI in the future — the AI that Schmidt, Greiner, the CCC, and other optimists envision — benefits the whole of humanity?

Respondents to the Edelman study champion regulation and transparency — over 50 percent say policies to guide AI’s development should be imposed by a “public body,” and say that it’s incumbent upon tech companies to show how AI will improve both lives and economies. And according to Katie Rae, who leads MIT’s The Engine startup accelerator, education will play a foundational role in shaping the next two decades in computing and AI research.

For its part, the CCC suggests that national AI labs could help address a shortfall of resources required to create more advanced systems. Microsoft CTO Kevin Scott summed it up nicely in a recent interview: “You can’t just create a bunch of tech and have it be super disruptive and not have any involvement … you have to create value in this world, and it can’t just be shareholder value,” he told VentureBeat. “I believe if you give people tools, the overwhelming majority of the uses to which they will be put are positive, and so you want to encourage that and protect against the negative in a thoughtful way.”

For AI coverage, send news tips to Khari Johnson and Kyle Wiggers — and be sure to bookmark our AI Channel.

Thanks for reading,

Kyle Wiggers
AI Staff Writer

P.S. Please enjoy this video of Stanley Systems’ automated robotic valet system.

[embedded content]

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Big Data – VentureBeat

Robots Must Ease Business Pains To Be Successful

March 13, 2019 SAP

Part 5 in the “RPA and AI in Finance” series, which examines the role that robotic process automation and artificial intelligence can play in finance operations

Everyone wants their own robot these days, to increase efficiency and free up time for work that matters. However, to many, “robot” remains a buzzword. While more people are starting to grasp the benefits of robotic process automation (RPA), it becomes even fuzzier when we start to talk about artificial intelligence (AI).

March 12 Fig 1 Robots Must Ease Business Pains To Be Successful

That’s why I’ve teamed up with Thomas Schultz and Enversion to break down how you can make AI work for you in practical terms, as opposed to just having it on your “I gotta have that, too” list. Last week we bridged the gap between RPA and AI, so hopefully you’re ready to start jumping into AI—but if not, read our article from last week, “A Tale of Robots: From Assembly Lines to Knowledge Workers.” The key takeaway is that we introduced to you a five-step model to tackle AI projects. Here’s where we start.

Step 1: Anchor the projects to true business pains

It seems like an obvious step, but if you’re not purposeful about why you need AI and what to use it for, then you’ll run into trouble. Here’s how we see it.

Challenge: AI is currently on the top of all hype curves. That makes it tempting to join the pack: “If they have one – we want one!” Even hard-core business experts are not immune to that psychology. Since most AI projects are not just a walk in the park – data collection and data preparation alone are expensive and resource-consuming tasks – you need to exhibit stamina and allocate the needed resources and executive attention.

That’s why you must choose a business pain that’s important to alleviate for everyone to have the guts to see it through and not just ditch it midway when the going gets tough.

Fix: Ask what true gains you will achieve by solving a problem with AI. Alternatively, ask what will happen if you do nothing. It’s simple: Even if it is “exciting AI stuff,” do your business-case homework. In short, you must treat an AI project like any other important project set up to solve real business pains. It’s fair that you might want to do some experimentation first, but if you’re serious about doing this, you must put your money where your mouth is.

Don’t let yours become another failed AI project

The reality is that many AI projects fail to deliver the envisioned benefits. That’s not just because you didn’t follow Step 1 above, but also simply because it’s still an immature technology where successful real-life application is still somewhat far away. That’s why there’s more to it than just Step 1, which we’ll uncover in future articles.

How is this relevant to finance, you might ask? Well, AI can do wonders for finance, as for any other function. Think about business intelligence as a self-service. It’s great that you have reports available at a click of a button, but what if you could simply ask a bot to figure out what happened, where it happened, and why it happened? That would save you a ton of time doing long-needed analyses and instead jump straight to what-if scenario-modeling and how to make it happen. Powerful, right? That’s just one of the potential uses of AI.

Have you already had thoughts about jumping into AI, or are you at an earlier stage where RPA or simple macros are the only “robots” working for you? If you have any examples of successful AI application, we’d love to hear about it. Please email us:

Anders Liu-Lindberg and Thomas Schultz

The next blog in this series will discuss how to succeed with AI.

This article originally appeared on LinkedIn and is republished by permission.

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Digitalist Magazine

Why CRM Must be an Ongoing Program, Not a Technology Implementation

December 4, 2018 CRM News and Info

Define, Build, Test, Deploy, Support….Upgrade. Do it all again in a few years.

Historically companies have allocated time and money to a CRM effort as a “one-time, big event.” Once live, the resources and dollars allocated were reduced to the bare minimum to simply maintain and support the CRM system until the next major upgrade.

It wasn’t long ago a company implementing CRM would complete the project, go-live with CRM, and be confident it would be 2 to 3 years before they needed to plan a major upgrade or to consider how newly released features or capabilities might be useful. Today, that timespan is as little as 6-9 months.

Times have changed. The “one time” mindset for CRM investment is no longer an acceptable approach.

What challenges are driving companies towards a CRM Program strategy?

Impact of SaaS

In the past, extensive planning was required for implementations and upgrades. It took time to purchase and prepare the physical hardware, install software for the servers, ensure backup and recovery processes were in place, and more. Now, thanks to SaaS platforms, a system can largely be turned on and off. Tasks that once took weeks or months of preparation are gone.

Now, you can visit a website and have your CRM software provisioned and available within hours, if not minutes.

Customer Expectations

There is no end in sight to the growing expectations of customers for companies to fulfill their needs and deliver great experiences. These expectations are increasingly redefined, forcing companies to continually look at customer-facing processes.

Companies can no longer dictate how and when customers communicate and must adapt on the fly.

User Empowerment and Adoption

Lack of user adoption has long plagued CRM implementations, and it doesn’t take many dissatisfied users to create a massive problem. Users want a successful CRM. They want to use technology to increase their productivity and make their lives easier. Users, like customers, are continuing to critically challenge the status quo by finding and implement their own solutions.

Without proper direction, and without an avenue for pursuing improvement, users will find alternative processes that work best for them.

App Stores

The ubiquitous use of smartphones and app stores have changed expectations of how technology should be consumed, and how quickly. In today’s environment when someone identifies a need, their first action may be to search for an “app” to solve the problem. As little as 2 to 3 years ago that was not the case.

The expectations for how we use, consume, and purchase technology for our personal use has pushed into our everyday business expectations.

Pace of Technology Change

Daily we see how technology continues to change and impact both our personal and professional lives. Technology is enabling us to do more things with vast amounts of data, to deliver more meaningful outcomes at a lower cost. In July, Microsoft announced its plan to provide two major CRM releases per year – April and October – offering new capabilities and functionality. The release notes associated with the October 2018 release is over 300 pages long.

To stay current, ongoing time for researching, prototyping, and planning the implementation of these new capabilities is required.

Agile Mindset

Companies are seeing the benefits of utilizing Agile methodologies. Having the flexibility to pivot, re-prioritize, and focus limited resources (time and money) on those things most important to the business is critical. Spending months in requirements and development followed by months of deployment, pushing potential return on investment to years after go-live, is just not acceptable anymore.

Stakeholders and sponsors of CRM need to be able to demonstrate its value as quickly as possible and to continually improve.

How do these challenges affect CRM strategies?

CRM must continuously improve

We are people of experience; we need to see it, touch it, and use it to determine if we like it and if it will meet our needs. When users see continuous improvement that makes them more effective at their job, their buy-in and contribution to new ideas will grow exponentially.

As described above, the volume and frequency of new CRM capabilities and new 3^rd party apps continue to grow. More and more capabilities are being infused with Microsoft’s Cognitive Services (AI) integration which creates new ways to sell, service, and market to customers. Companies must consider how these capabilities could transform their business or they risk being left behind competitors.

The only way to capitalize on these improvements is to have an ongoing program for proactively managing change.

How to build a successful CRM Ongoing Program, not just a technology implementation

We believe organizations need to have a different mindset and approach to their CRM investments. In our experience, organizations implementing new cultures and ongoing continuous improvement programs vs. one-time CRM technology solutions have the highest adoption rates and are seeing the best return on their investments.

In part two of this blog series, we will take a deeper look at what characteristics, behaviors, and attributes need to exist for a company to implement and build a CRM program and not just implement a technology solution.

By Jeremy Cox, Crowe LLP, www.crowecrm.com

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CRM Software Blog | Dynamics 365

Must Chatbots Be Voice-Enabled To Improve The Customer Experience?

August 31, 2018 SAP

Digital transformation has forged new pathways for customer relations, allowing organizations to reach their target audiences using emerging technologies that provide instant, easy, and relevant customer experiences. These dramatic advancements in technology have changed customer attitudes toward service times, delivery, and accessibility, especially in the realm of e-commerce. To adapt to customer expectations, companies like Amazon offer Echo, a device that connects to Alexa Voice, enabling customers to access information and complete tasks without having to press a button.

Speed and efficiency is valuable to customers, and this provides the potential for organizations to capitalize on conversational artificial intelligence in retail. Voice-activated chatbots are much more than a fading tech trend, and with companies like Facebook offering businesses text-based and voice-activated communication through Messenger, conversational AI will significantly impact the way customers choose to interact with retailers.

Create a personalized service

Chatbots simulate an in-store experience for customers buying online, ensuring that customer service is cohesive and consistent across the brand. This kind of automation creates a tailored purchasing experience on mobile and web devices, allowing customers to speak to chatbots as they would sales associates. Chatbots can be used to locate and sell products, answer product-related questions, direct customers to helpful web pages, and upsell products with the added benefit of immediacy and accuracy.

Chatbots are the new user interface

Mobile apps are less relevant. unless they’re for messaging. With mobile phone messaging apps estimated to increase to 2.5 billion users worldwide in 2021, retailers have a wider reach if they can integrate chatbots into customer service. Conversational AI already exists within our smartphones with Siri, Bixby, and Google Assistant. Improvements to natural language processing (NLP) and understanding make voice the new UI. It can respond to more complex questions and complete simple request commands three times faster than texting. That means that retailers need to prepare for the shift in demand, especially in providing seamless customer experiences in e-commerce, even as they ask ”Is it good enough yet?” “Should I focus on other priorities until it’s better?”

The Intelligent Enterprise and conversational AI

Conversational AI will make managing operations more efficient and comprehensive by providing real-time data, accessing information on stock and ordering, volumes, sales figures, and HR management through voice-activated commands. Retailers can build their own chatbots dedicated to specific customer-directed aims and customize NLP models dependent on their performance needs. With growing demand for voice-enabled technologies, conversational AI isn’t an option, but an increasing necessity, in the retail industry.

Of course, there will be discussions about whether voice-enabled commerce is good enough yet, but regardless of its current accuracy, retailers should start planning and preparing for it now.

To learn more about SAP Leonardo Conversational AI Foundation.

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Digitalist Magazine

Automakers must decide how much uncertainty is acceptable for autonomous vehicles

June 27, 2018 Big Data

To meet the goal of autonomous vehicles that can operate safely and without any need for human input — that is, L5 automation — automakers must train AI systems to navigate myriad conditions they’ll run into in the real world so that they don’t actually run into anything in the real world). Our highways and roads are, as we all know from experience behind the wheel, wholly unpredictable places, and they’ll continually require self-driving cars to instantly interpret and react to “edge case” scenarios.

While machine learning can guide AI to develop a recognition of, and reaction to, scenarios that it has seen many times before, there’s an immense hurdle in training AI for one-in-a-million (or billion) situations. For example, AI may be well-versed in basic freeway driving, or identifying pedestrians under expected circumstances. However, edge cases abound. Freeways may be littered with everything from tire scraps to sofas to grandmothers chasing after ducks; Halloween costumes can make pedestrians difficult to detect; you can set traps for autonomous vehicles; and even electric scooters can prove problematic for AVs. There will always exist “unknown unknowns” that companies cannot simulate because no one could foresee what to simulate.

Successfully addressing the unpredictable is particularly important to autonomous driving because any and every unexpected and undetected obstacle is potentially a life-or-death safety issue. Because of this, the stakes for addressing edge cases are enormous for the industry.

Solving deep learning’s edge case constraints

The application of deep learning to solving these edge cases snags on a major issue: Deep learning isn’t well suited to provide such assurances. While it’s possible to determine an AI application’s accuracy against a known dataset, there’s no guarantee of performance in real-world situations where edge cases occur and unfamiliar data must be processed. Deep learning systems deliver stunning results and beat expectations when dealing with datasets that are very similar to what they’ve previously encountered. But, because they have limited abilities to extrapolate information, there’s no way to predict how they’ll function in those outlier scenarios. In fact, a good deal of deep learning theory supports the idea that, at some level, it’s not really possible for these systems to understand a domain of data different from what they’ve been trained on.

Faced with this limitation, the most successful strategy today is simply to provide an AI application with huge amounts of data so that it becomes familiar with as many potential edge cases as possible. This brute force method calls for cars to be driven all the time to build experience with ever more unfamiliar scenarios, and then have that data added to the system. Doing so then requires that autonomous vehicle system manufacturers have the infrastructure to label and support those incredible volumes of data.

However, even if manufacturers could have that near-infinite amount of data right this minute, it would still be impossible to prepare autonomous cars for everything they might see on the roads. The world changes, human behavior changes, new cars and objects (like the aforementioned scooters) are introduced, etc. Given this reality, there’s a tremendous onus placed on vehicle system manufacturers to determine how they’ll ultimately address the uncertainty of the real world, while providing a safe and comfortable autonomous vehicle experience that isn’t littered with false positives (i.e., unnecessary braking) for every unknown.

Establishing ways to measure success

Today, AV industry players naturally take an adversarial stance in competing over who has the best technology. However, the questions faced here are so existential and challenging — such as determining how best to test whether autonomous vehicle technology is truly ready for safe use — that industry leaders will likely need to band together to answer them. At the same time, customers and society as a whole need to be convinced that this technology is safe and beneficial. I believe an open standard, one established by the industry to test and verify the safety of autonomous vehicles, could help serve this purpose. Collaboration and edge case data sharing might very well be the best strategy for everyone.

As to the level of safety that autonomous vehicles must achieve, it’s important to recognize two things. First, humans are very good drivers; we only have one accident every 165,000 miles. Second, there’s a double standard in our expectations when it comes to humans and machines. We expect humans to make mistakes, but we find it much less acceptable when machines fail. Given these expectations, autonomous cars may need to be 10 times safer than human drivers to earn widespread acceptance. While we’re likely years away from seeing L5 fully autonomous vehicles able to navigate through the infinite edge case scenarios the roadways throw at them, we’ll be an order of magnitude safer when we get there.

Alexandr Wang is the CEO of Scale, a company that accelerates the development of AI by democratizing access to intelligent data.

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Big Data – VentureBeat

6 questions you must answer to identify your best way to implement AI

June 15, 2018 Big Data

Commodity artificial intelligence-as-a-Service (AI-aaS) offerings are popping up everywhere. Just as you can whip out a credit card and spin up a virtual data center in Amazon, Microsoft, or Google’s cloud, you can now call on previously trained machine learning clusters to handle your AI chores.

Using an API, you can upload a photo library to Google Cloud Vision or Amazon Rekognition to have the program scan it for objects, faces, logos, or terms of service violations in seconds, for fractions of a penny per image. Any business can now deploy the same technology used by the Google Photos app and Amazon Prime Photos to automatically categorize and label smartphone snaps based on the people, objects, and landmarks inside them.

Real estate companies use image recognition to allow prospective home buyers to search for houses whose appearance pleases them. Car companies like Kia use AI to customize marketing campaigns based on the photos people post to social media. Cities can also use the technology to understand traffic patterns and make better decisions about infrastructure projects. And so on.

This all sounds wonderful, revolutionary, and scalable, but as with other commoditized technologies, the off-the-shelf, one-size-fits-all approach doesn’t work for all companies or business goals, which raises the question: For your AI needs, should you choose a commodity cloud AI service or opt for a more comprehensive custom solution? As AI becomes more and more critical to businesses, three basic options have emerged:

Use a commodity AI-aaS offerings such as Amazon AI (including Rekognition), Clarifai, CloudSight, Google Cloud Vision, IBM Watson, or Microsoft Cognitive Services. These offer a relatively narrow range of AI functions, mostly enabled via APIs for text and image recognition, as well as natural language processing (NLP).
Engage third-party applied AI companies that specialize in a broader and more customized range of vertical AI services. This sometimes involves an on-premises solution for companies that don’t want to share their data in the cloud, or focuses on a particular vertical, such as finance, health care, marketing, or retail.
Build out a full-stack machine learning system from scratch, using your own experts and data. This is by far the most complex option and is primarily for organizations where AI is essential to their core value and revenue.

Each of these options makes sense for certain kinds of business users. Exactly which one is your best option depends on how you answer the following questions.

1. What kind of AI jobs do you need to do?

AI is helpful in a wide range of business use cases, including predictive analytics, forecasting, process optimization, personalization, and many others. But while IBM Watson offers some additional analytics and language processing tools, many commodity AI-aaS vendors are focused on the tasks most commonly associated with machine learning: text and image recognition. These serve as out-of-the-box solutions for specific, narrow tasks for organizations that have main functions that do not center around AI — say, a local law enforcement authority that wants to quickly scan image databases against a picture via facial recognition, or editorial sites that want to moderate comment sections (or images) for objectionable content.

If you have any other or more complex AI needs in addition to those clearly defined tasks, or massive amounts of data (proprietary or otherwise), you’ll likely want to engage an applied AI partner, or embark on your own internal full-stack AI setup (more on that later).

2. What kind of volume can you afford?

Image and text recognition services are increasingly commoditized, and sometimes even free at low volumes. But if you’re doing them at scale, the costs can grow exponentially.

Say you’re running a small photo-sharing service and need to scan and analyze 10,000 individual images a month to ensure they don’t contain objectionable content. On Amazon Rekognition that would cost $ 10; Google Cloud Vision would charge you $ 13.50, but that also includes label detection (i.e., identifying whether it’s a picture of a cat, a bicycle, a bagel, etc). Label detection would also be useful for, say, realtors who want to flag kitchens featuring particular types of cabinets or countertops, or doctors who need to identify different types of skin lesions.

If you were operating on the scale of Pinterest, however, whose users upload 14 million images a day, the economics of image safety search would change significantly. Even at the steeper discounts offered for large volumes of images, it would cost a service of that size about $ 16,500 a day — just over $ 5.1 million a year — with Google Cloud Vision.* Using Amazon would cost $ 10,600 per day and around $ 2.3 million annually.

Of course, the cost also goes up depending on how much information you’re asking the AI to provide. At its steepest discount, Google Cloud Vision adds another $ 0.0006 per image for detecting text, plus the same amount for detecting faces, logos, and landmarks, respectively; add all that to labeling and content scanning, and a service on the scale of Pinterest is looking at spending more than $ 17.6 million annually.

Suddenly those inexpensive commodity cloud services don’t seem so cheap anymore.

3. How good do the results have to be?

Though commodity AI-aaS machine learning models have been trained against very large data sets — as when Google used 200,000 images from the Metropolitan Museum of Art to train its BigQuery engine — that doesn’t mean they’re always going to produce accurate results.

Upwork recently published a comparison of six leading image recognition APIs to gauge how accurate they are at labeling images of animals, people, text, and objects. The test wasn’t rigorously scientific, but the results were fascinating.

Each AI engine’s predictions were on target with some images and far off base with others. For example, all excelled at identifying a parked car on an urban street, but some stumbled when shown two cats, the Grand Canyon, a bottle of wine, or three people standing on a sidewalk.

Shown a realistic portrait of a Western frontiersman leading his pack-laden horse, Google CV correctly identified it as a painting, while Watson suggested “camel racing” and Microsoft’s best guess was the surreal “person riding a surfboard on top of a book.”

A big advantage to going with an applied AI solutions provider or consultant (or running your own AI stack) is the ability to train the machine learning models in more customized ways and fine-tune the results to increase accuracy. For example, if you’re building a wine recommendations app, instead of just labeling a bottle as “wine” or “pinot noir,” you might want to drill down into more specifics, such as the vintner, region, or vintage. Or if, say, you’re a brewer who wants to automatically identify your beer’s logos on social media images even when they’re only partially showing and the bottles are tipped over — a stiff challenge in the facial and image recognition process known as occlusion — then you would benefit from an applied AI or DIY full-stack solution.

4. How much flexibility do you require?

Commodity AI-aaS offers far less control and flexibility than an applied AI or in-house full-stack solution in other ways, too. For example, Amazon Rekognition offers thousands of image labels, but not always ones your business needs. Amazon might be able to tag “kitchen” or “sink,” for example, but not necessarily “Kohl faucet” or “tile backsplash”. To add new labels or change how Amazon flags images for potentially objectionable content, you’ll need to request it. Amazon requires six to eight weeks to add new types of moderated content and does not promise to honor all requests.

Google Cloud Vision places limits on the size and number of images you can feed through the API at any time, and all services limit the kinds of files they will accept and types of data they can recognize. Amazon accepts only PNG and JPEG files, for example. Only three of the six AI-aaS vendors mentioned here offer optical character recognition (OCR) along with image recognition; only Clarifai accepts video as well as still images. In other words, if all your real- estate images are in RAW format, you may need to convert them first. If you want a service that reads the labels on images of wine bottles, you’ll want OCR.

The old Henry Ford line about how you can have a Model T in any color (as long as it’s black) applies to AI as a service — your options will be limited.

5. What kind of performance do you need?

Latency is the quiet killer for applications that require near-real-time image or text processing. Clarifai notes that its API responds within 200 to 400 milliseconds for a single image sent from inside the United States; add more images or video, or increase your distance, and the latency grows worse. CloudSight, on the other hand, needs from 6 to 12 seconds to respond, possibly because it relies on human crowdsourcing to manually tag some images.

As with all cloud services, reliability is also an issue; your ability to process text or images is entirely dependent on the availability of third-party servers. Anyone who’s suffered through the rare AWS or Google outage can tell you how frustrating that can be. Even one extended outage is one too many.

Having an AI stack on-site will largely negate the latency issue and give you more control over availability.

6. How much in-house expertise do you have?

AI engineers are in huge demand. Many organizations simply don’t have the necessary talent on hand, and recruiting that talent means competing for candidates with companies such as Google, Microsoft, Facebook, and Amazon, which are aggressively investing and innovating in the AI arena. And even if you do have the resources to hire top AI engineers, you’ll still have trouble finding ones who have domain expertise around your particular business.

If you’re just experimenting with incorporating AI into your business, or you want to offer basic low-volume AI functionality as a service to customers, then cloud-based services can be a good way to get started. But if you need more scale, greater flexibility, domain expertise, data privacy, or services that a commodity cloud service doesn’t offer, and you don’t have the desire or resources to recruit and hire a full AI team in-house, then finding a third-party applied AI provider is probably a better way to go.

While ramping up will be a business and technological challenge, creating your own full AI stack can be significantly advantageous for your organization in the long run, if AI is your core value. But for everyone else, getting on board with a AI-aaS solution or applied AI partner is essential. As noted by Harvard Business Review, AI is poised to be a transformational technology — on a par with the steam engine, electricity, and the internet. Organizations that don’t get ahead of that train are in danger of being run down.

*Google Cloud Vision’s pricing only accounts for volumes up to 20 million images per month. Presumably, there are discounts for higher volumes available upon request, but even then, the expense is considerable.

Ken Weiner is CTO at GumGum, an applied computer vision company.

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Big Data – VentureBeat

Tag Archives: Must

I MUST BE A RACIST BECAUSE I, TOO, SUFFER FROM “NEGRO FATIGUE”

Businesses and Employees Must Prepare for W-4 Changes Coming in January 2020

AI Weekly: A growing chorus of experts agrees facial recognition systems must be regulated

Why DBAs Must Embrace DevOps

Commentary Competition

AI’s many challenges must be thoughtfully addressed

Robots Must Ease Business Pains To Be Successful

Step 1: Anchor the projects to true business pains

Don’t let yours become another failed AI project

Why CRM Must be an Ongoing Program, Not a Technology Implementation

Must Chatbots Be Voice-Enabled To Improve The Customer Experience?

Create a personalized service

Chatbots are the new user interface

The Intelligent Enterprise and conversational AI

Automakers must decide how much uncertainty is acceptable for autonomous vehicles

Solving deep learning’s edge case constraints

Establishing ways to measure success

6 questions you must answer to identify your best way to implement AI

1. What kind of AI jobs do you need to do?

2. What kind of volume can you afford?

3. How good do the results have to be?

4. How much flexibility do you require?

5. What kind of performance do you need?

6. How much in-house expertise do you have?

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