Month: February 2019

No real surprises in a February 22 Information Age article by Andrew Ross discussing issues relating to RPA implementation, but he does point to the biggest issue facing all autonomous technology uptake: understanding what needs to get done and how.

As “swivel chair” software, Robotic Process Automation (RPA) promises to be a game changer for small to midsize businesses looking to increase worker productivity by reducing the amount of time they spend doing human-as-machine labor. But many workflow systems have developed organically, over time. This organic growth incorporates a lot of activity that may no longer make sense. Automating the process as-is can build in inefficiencies.

But Ross’s article illuminates the big problem facing business owners: Alex Rinke, CEO of Celonis, told Information Age he’s “heard of many failed RPA initiatives; and it’s often because enterprises fail to understand their inefficiencies before implementing automation.”

There’s no doubt about it, RPAs and Machine Learning are going to radically improve the speed of your business. But you need to work closely with system specialists like ATC to understand what needs to get done and how best to do it.

NYU professor Amy Webb is coming out with a new book on March 5th, The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity.

The central tenet of the book is that AI is currently being developed by commercial enterprises (the G-MAFIA–Google, Microsoft, Amazon, Facebook, IBM and Apple) in the US and Tencent, Alibaba and Baidu in China, while the US government has decided to turn its back on research funding, and Europe is figuring out the web.

My favorite anecdote from the congressional Facebook hearings is a septuagenarian senator demanding of Mark Zuckerberg, “How do you make money with this thing?” To which a stunned Zuckerberg replied, “…We sell advertising, senator.”

This laissez faire attitude on the part of governments is a mistake, Webb, asserts, because these businesses are only interested (as they should be) in turning these technologies toward profit.

She suggests an international guiding AI body be created, GAIA (Global Alliance on Intelligence Augmentation), to ensure societal benefit from the implementation of AI systems. AI is a wave that has not even created a visible swell on the horizon. It would be far better for governments and societies to be prepared than to let that wave hit the beach at full force without any preparation.

As AI is only currently applied to rule oriented data systems, it feels distant. But as the learning sets grow, and applications increase in complexity, we will see more and more life quality systems subsumed by this nascent wave.

In the recent O’Rielly whitepaper, “The Path to Predictive Analytics and Machine Learning,” the authors , Conor Doherty, Steven Camina, Kevin White and Gary Orenstein, point out a key issue facing many businesses: data silos.

What is a data silo?

Traditional data architectures use a siloed, Online Transaction Processing (OLTP) model for Customer Relations Management (sales, returns, queries), and a completely separate data store for analysis (if these are accessible online, they are called Online Analytical Processing (OLAP) warehouses.

OLAP-optimized data warehouses cannot handle one-off inserts and updates. Instead, data must be organized and loaded all at once —as a large batch—which results in an offline operation that runs overnight or during off-hours. The tradeoff with this approach is that streaming data cannot be queried by the analytical database until a batch load runs. With such an architecture, standing up a real-time application or enabling analyst to query your freshest dataset cannot be achieved.

Doherty Camina, White, Orenstein

The O’Rielly team suggest partnering the somewhat alarmingly named Apache Kafka, a high-throughput, distributed messaging system that “acts as a broker between producers (processes that publish their records to a topic) and consumers (processes that subscribe to one or more topics)” with Apache Spark, a distributed, memory-optimized system for data transformation.

envision a shipping network in which the schedules and routes are determined programmatically by using predictive models. The models might take weather and traffic data and combine them with past shipping logs to predict the time and route that will result in the most efficient delivery. In this case, day-to-day operations are contingent on the results of analytic predictive models. This kind of on-the-fly automated optimization is not possible when transactions and analytics happen in separate siloes.

Doherty, Camina, White, Orenstein

While your company might not be ready for real time analytics, its worth thinking about building a system ready for transformation. And good news for your bottom line, Apache Kafka and Spark are both free.

We hear a lot about Big Data. What people don’t talk about much is that storage of all that data is going to be extremely costly, both financially and ecologically.

But nature, from the beginning, developed the most efficient memory storage medium in existence. The DNA of one human, as Riccardo Sabatini dramatically presented in a 2016 TED talk, fits into 175 printed volumes that are each 1,600 pages long, with a font size of 6 (1/2 of what you’re reading). He also pointed out that the information representing every molecular aspect of a newborn baby would fit on 2000 Titanics packed with thumb drives.

In 2012 two Harvard scientists, George Church and Sri Kosuri, figured out how to translate data into DNA. Since then, DARPA has granted a lot of money to companies and university researchers to figure out how to make DNA data storage a reality.

Why? DNA is shelf-stable. It can last, as we know from Woolly Mammoth samples, 60,000 years. DNA is not electrically or magnetically activated. DNA is also ridiculously dense. To illustrate: every film ever made could be saved on a pile of DNA the size of a sugar cube.

To take it even further, every piece of data currently in existence, stored on DNA, would fit in a single, average sized closet, requiring no fans, cooling systems, electricity or hardware. It would never degrade.

Right now, a company called Catalog is building a concept machine that is the Guttenberg printing press of DNA memory storage. The size of a school-bus, the encoder/decoder processes base pairs like movable type. Instead of the insanely costly early methods, Catalog uses shorthand to encode binary sequences.

If you hired Catalog to put every photo, movie, piece of writing, homework, report card, textbook, tv show, novel, social media post and medical record you’d ever generated or consumed in your life–basically everything that can be recorded–into a bunch of strands of DNA, it would fit in a drop of water. And it would last basically forever.

Google and Microsoft are both researching DNA storage. We need it now.

Brian Bergstein, in the article “This is why AI has yet to Reshape Most Businesses” in MIT Technology Review, comes to the conclusion that most companies don’t have the time or resources for enacting AI programs.

AI is expensive, and unless managers can see a real need for it, it will remain a technology used only by the most profitable and data driven industries.

But that doesn’t mean that you should be complacent about other autonomous technologies. Achim Daub, a perfume executive says, “no one really has time to do greenfield learning on the side.” He was speaking about AI development, but I couldn’t disagree more. It is precisely ‘greenfield’ exploratory learning from which your company’s biggest transformation will arise.

If your VP of Sales was able to compile reports in seconds rather than days, would that transform your business? Of course it would. Robotic Process Automation is a relatively new technology that most managers haven’t had time to learn about. Set your business’ managers free to learn what RPAs, robotics and machine learning could do for your company. Let ATC be their guide.

When I was working with McKinsey & Co., the most repeated piece of information in efficiency studies was that people can only really work productively in an office for about three hours each day. The rest of the time is spent doing tasks that don’t require human-as-human labor. As those tasks shrink–consider the assistant whose day used to be spent retyping, copying and paper filing–what would you like your employees to be doing with their time?

Humans are much better than computers at learning, and learning is the one redirect that can effectively refocus your employees once their initial “three hours” is up. At ATC, we suggest at least two hours per week per employee of professional development time to learn new software, keep up-to-date on industry innovation and improve skills. But we also suggest three hours per week of exploratory learning.

Were you to include exploratory learning as part of your office workers’ job requirements, what would that look like, what would it do, and what metric could you use to measure it?

“Old dog” employees, those who just want to run down the clock to retirement, are least likely to take advantage of exploratory learning. Also, unmotivated “users” will see exploratory learning as an opportunity to goof off. They see their employment as a transactional scam anyway. You take their time and they take your money, and they are always interested in giving up less time and getting more money.

But employees who believe in your company’s mission, who are able to envision growth in their job and eventual promotion, will use every second of exploratory learning becoming a better employee.

Exploratory learning is just that. It is not professional development, where, say, someone in accounting takes an IRS information course. It is when someone in accounting decides to learn 3D modelling or takes violin lessons.

What could this possibly do for your company? Humans have been building tools since the dawn of time. But the stick you use to hit an enemy is also useful as a lever, a walking aid and a probe. 3D modelling, for example, triggers connections between the concrete and conceptual structuring capacities of the brain. Musical notation is a value coordinate system of unsurpassed nuance. Study in either of these fields can lead to important breakthroughs in approaching problem solving in the office. Also, by telling employees that you want them to learn new tricks, they know you are investing in them. In very real terms, this leads to greater employee loyalty.

How do you measure it? Before implementing any digital transformation, ATC would recommend you take a baseline survey of your employees querying everything from productivity and workload estimates to happiness and quality of life evaluations. Once you have that baseline, introduce exploratory learning and regular professional development and see how they feel in six months.

We think you won’t have to take a survey. You’ll see the increased productivity on your P&L report.

In a 2/13/19 article on questions about Universal Basic Income (UBI), Kelsey Piper discusses issues surrounding Universal Basic Income and what it means after reading a new National Bureau of Economic Research (NBER) working paper by Hilary Hoynes and Jesse Rothstein .

The working paper raises questions about what problem UBI is meant to fix.

Are we looking for a UBI to increase labor market participation? Leave it the same? Decrease it? Do we want a UBI in order to fix welfare disincentives to work, or in order to fix the fact that people have to work to survive?

Kelsey Piper

But while the study and Piper are ostensibly well-intentioned, I’m not sure the right questions are being asked.

Most technologists are painfully aware of the likely effects of the coming wave of autonomous technology on human-as-machine labor. As I’ve discussed elsewhere, endless growth compensating for technological disemployment is not a realistic construct.

Human-as-Machine labor jobs will decrease. As these are the jobs that have been highly valued as the means of production for the last 250 years, the question facing our received notions of culture is really, “without these jobs, what will people do to live?” And how will we, as a society value what they do?

In a recent conversation, a colleague pointed out that with all this new tech, new jobs will be created. Of course that is true. But how many? When I was starting out in relational databases, it was “new tech” to most business development people. But I was simply doing what a roomful of clerks might have done ten or fifteen years before.

For one of the businesses I own, we used to employ four office workers beyond two executives. One assistant/secretary, two copyright clerks, and one “gopher” who made copies of audiotapes, bought microphones, and did deliveries. Beyond those workers, there was the phone vendor, the copy-machine service guy, the computer guy and a cleaner. Today, twenty years later, we have one employee, and that’s me. And I work from home. I can do all of those things faster and without getting up from my desk. Yes, the business is much more profitable, but none of those people is making money.

You might point out that people built the software and internet businesses I use, and they make money. Yes, but the workers who were replaced by those technologies would not have been able to become software developers. Fortunately, they were nearing retirement and were able to work for us until they no longer wished to.

So what will all these people do? The answer is that non-information economy workers will have to find a new purpose for their lives. And they will need money to spend to support economic growth.

In a recent conversation with David Chou on the excellent podcast IT Visionaries, Ian distilled a key takeaway.

The CIO needs to understand what metric the CEO is using.

The IT budget at most organizations is a considerable portion of operating costs, Chou explained, and most CEOs are interested in reducing costs: “If they can shave 2%, 5% off IT costs, that’s a lot of money.”

But Chou explained that at Kansas City Children’s hospital, just by building a customer facing scheduling tool for appointments, they were able to increase the number of patients seen by 17%.

Since hospitals earn money based on how many patients they see, this was a massive improvement to the hospital’s bottom line. Chou was quick to point out to the CEO that if he could have 1/2 of that new revenue, he could do even more.

Good executives everywhere know their business. But what they know is backward facing. They know what was done. What worked in the past. Because they have never seen the kind of digital transformation occurring in what Gartner calls the “4th Industrial Revolution,” they have no frame of reference for understanding how automated technology could revolutionize what they do and how they do it.

Chou says the “I” in CIO should also stand for “Influence.” It is imperative for CIOs to think bigger, and understand that the CEO has one core metric–the bottom line.

When CIOs are busy “keeping the lights on,” it’s hard to convey just how transformative IT can be. But we are at a point where this 4th Industrial Revolution is in the process of changing things on the order of the introduction of the telephone at the end of the 19th century.

From the white paper Gartner Top 10 Strategic Technology Trends for 2019 by Kasey Panetta:

The Gartner Top 10 Strategic Technology trends highlight changing or not yet widely recognized trends that will impact and transform industries through 2023.

Whether it’s cars, robots or agriculture, autonomous things use AI to perform tasks traditionally done by humans. The sophistication of the intelligence varies, but all autonomous things use AI to interact more naturally with their environments.

Autonomous things exist across five types:

• Robotics
• Vehicles
• Drones
• Appliances
• Agents

Those five types occupy four environments: Sea, land, air and digital. They all operate with varying degrees of capability, coordination and intelligence. For example, they can span a drone operated in the air with human-assistance to a farming robot operating completely autonomously in a field. This paints a broad picture of potential applications, and virtually every application, service and IoT object will incorporate some form of AI to automate or augment processes or human actions. Collaborative autonomous things such as drone swarms will increasingly drive the future of AI systems

Explore the possibilities of AI-driven autonomous capabilities in any physical object in your organization or customer environment, but keep in mind these devices are best used for narrowly defined purposes. They do not have the same capability as a human brain for decision making, intelligence or general-purpose learning.

I was struck by how a piece by the late Oliver Sacks in the February 11 New Yorker, “The Machine Stops” engages alienation anxiety via E.M. Forster’s story of the same title.

Sacks is writing at the end of his life of how “Every minute, every second, has to be spent with one’s device clutched in one’s hand. Those trapped in th[e] virtual world are never alone, never able to concentrate and appreciate, in their own way, silently. They have given up, to a great extent, the amenities and achievements of civilization: solitude and leisure, the sanction to be oneself, truly absorbed, whether in contemplating a work of art, a scientific theory, a sunset, or the face of one’s beloved.”

While Sacks was, in his own estimation, a grumpy old man, out of touch with technology, I’m sorry he didn’t see the promise of these “devices.” But neither did he suspect that we are on the cusp of a revolution far more disruptive to Western culture than the industrial revolution that gave birth to the technology he wistfully longs for in his piece–steam engines.

Much as the generation before his viewed the telephone as an unforgivable intrusion into home and family life, they ultimately realized it was a necessity. Not just for them to speak with distant family members, but also to conduct business without having to leave their families to go the office.

The earliest marketing for mobile technology touted its ability to allow us to spend time with our children when otherwise we would be stuck at the office. “Mommy, I want to go to the beach!” a child told her paper-shuffling, executive mother. With her trusty 8 lb. laptop, however, she was able to work on the beach (at least for forty five minutes before her battery died).

People might have seen me hunched over my smart phone as my son and I stood in the garden at Chenonceau–a chateau in the Loire Valley–and shaken their heads. Here’s a typical American, unable to “be” here and appreciate it with his son.

The only thing I can say is that ten years ago I would never have been able to be there with my son at all. I would have been sitting in an office in New York making phone calls and reading emails, taking care of business. While it may be unpleasant to consider how prevalent these infernal devices are, it might be better to consider how unpleasant it would be without them.

But more central to my concern with Sacks and Forster’s dystopian visions is that what they observed and were critiquing, and what made me have to take time to send emails and spreadsheets while in the garden at Chenonceau, was the use of connective technology under the rubric of alienated Capitalist valuation structures.

Why does Forster’s Kuno, separated from his mother by “The Machine,” not live with or see his mother? It’s because Capital, or Capital’s evil twin, Totalitarianism, deemed that mother and son are more productive separated.

People living through the height of the industrial revolution and human-as-machine labor saw how dehumanizing it was. They saw technology as the disruptor, but the disruptor was Capital. When people are worth less than the goods they manufacture, when they have little to no physical or emotional connection to the work they do, they become alienated.

We are in a transitional phase. I nervously check my phone all the time because I am anxious that at any moment something might happen at work that will derail my business and livelihood–and I am comparatively well off. I am also eager to stay ahead of my competition. These are not healthy attitudes that can go on forever.

We must use the next phase of technology to unplug. I would not have to “stay on top” of so many things if I had an AI assistant capable of performing the more mechanical tasks that take a lot of my time. But more importantly, if I knew that I and my family were secure via a social safety net and we had a culture that respected human beings for what they knew and how they acted rather than how much money they had accumulated, I wouldn’t need to be anxious at all.