AI, Big data, Automation,
I wanted to write a quick primer you could send a non-technical executive that would allow them to quickly understand some popular terms, applications and the key vendors associated with each.
If you are trying to move any AI related initiative forward you need to get the business on your side. So create some simple summaries for executives in your organization to understand what can be done and what partners and vendors you need to know about.
I admit to mostly enjoy looking forward. Looking back can be cringe-worthy - parachute pants and Vanilla Ice and so forth. Like a lot of people I got into tech because I prefer to think about what can be done versus what was done.
So it was a total accident when I stumbled upon this Advertising Age article that used a few quotes from an interview that I did in 2003. At the time I was pretty focused on using data analytics to do a better job of online marketing. A lot of what is second nature and obvious today was still new or even future tech then.
Stone Age Analytics - 2003 AD
The Early 2000s Was a Simpler Time for TV but not analytics. The interview came from work I was doing looking at advertising optimization. We were doing early work into slicing our analysis by the viewer's time. Advertising and commercial analytics tool sets were primitive. The company I was working with provided a broad range of outsourced marketing and advertising services from ecommerce (we had just launched the company's first platform), to direct mail - yes, postal mail, to outbound and inbound customer service and sales calls and marketing creative services.
I had a million square feet of fulfillment and 600 or so call center seats. The systems were on different hardware platforms and my office was in a raised floor data center with server racks as my only view. Mid-range and PC platforms were everywhere. UNIX boxes, IBM mid-ranges and so on. Integration involved a lot of transfers of files, especially CSV and even some email and FTP.
Mostly it involved us creating our first data warehouse by hand writing ETL and then using shockingly complex SQL statements to get at data we wanted. There was no meta data for most of the sources and we dealt with very different data standards used by our clients, their vendors and partners and the laundry list of agencies and data service companies we worked with to enrich or validate our data.
Insights From The Big Data Dark Ages
The point of this was mostly looking for usable patterns in the data. Once we found data we wanted to use we would then move some logic to code. The more we looked the more interesting stuff we found. It was real analytics and it certainly was Big Data although nobody had coined that term yet. One thing is wasn't was Artificial Intelligence or Machine Learning.
We iterated our way through the analytics, usually coding and scripting until the data looked usable and then trying to figure out if we had found what we were looking for. Half the time it was difficult to know what to do with the insights and I never quite knew how much to trust it.
Fast forward to today and the tools and, especially the code, have changed a lot. It certainly makes what we were doing look primitive, inefficient and superficial. Harnessing and accessing Big Data has improved dramatically, analytics tool sets can automatically generate insights it took us days or weeks to get at and code learns through iteration the way.
We Have Learned But We Haven't Evolved
Recently MIT Sloan published a story illustrating how AI will likely widen the growing gap between our ability to leverage analytics and Big Data to generate insights on the one hand and the ability of managers to do something useful with them on the other.
We need solutions to address the fact that accelerating developments of all sorts are going to increasingly make people the weak link in an ever stronger chain. I think the skillsets of the manager of the future need to look a lot different or we risk dependency on systems that will lead to a lot of lost opportunity.
Gatorade commissioned this machine for a commercial. It took 5000 hours to build and uses 2048 nozzles that release water with millisecond timing and strobe lights to create animation from the reflections. This is not special effects or CGI.
Finance is a critical industry in the development and commercialization of AI trends in general and near-term machine learning applications specifically. JP Morgan's May report ‘Big Data and AI Strategies - Machine Learning and Alternative Data Approach to Investing' was intended to focus on technical applications of big data on investment analysis. But as this news story on efinancialcareers points out it is extremely valuable as a way to articulate the skills needed to execute the strategy.
Machine learning is a great technology that is moving really quickly. But the direction is also clear. I said before that every company needs to look at how artificial intelligence will be applied to their business. A half-joking point I made was that everyone's job becomes learning how to use AI to eliminate other people's jobs. Artificial intelligence isn't going away and regulation, ethic or ignoring the impact on the jobs market won't move us forward. This disruption is going to be more transformative than previous technological disruptions including smart phones, the internet, ecommerce or the PC revolution because it will destroy jobs at a much faster rate than it creates them and it will also destroy high paying, professional jobs as well. It is time to make it a national priority to decide how we handle it.
Human cloning has been the subject of plenty of science fiction but the process is extremely complex. It has become one of those generations is just a decade or so away....every decade. In my opinion this isn't where the action is. The applications are not that valuable and the costs, complexity and ethical roadblocks slow it every step of the way.
Gene manipulation has many more applications and is already ongoing. By reprogramming genes we can seemingly address an amazing range of conditions, ailments and maladies. Unlike cloning this has direct and extremely valuable commercial applications. So I suspect that while we watch and fret over cloning, gene manipulation will help us to build a better future.
When someone is 100% right and still fails to win an argument decisively ask yourself why. The data is conclusive. Increases in greenhouse gas emissions are in fact making the earth warmer. The reason why there is such a concerted effort to deny, obfuscate and distract from the issues is in part because of Paul Ehrlich.
The worst thing to ever happen to the environmental movement and climate change in particular was Paul Ehrlich's The Population Bomb. Among other things he predicted there would be no UK in the year 2000; that mass starvation was unavoidable; India would be decimated and 60 million would starve in the US alone. Ehrlich wasn't alone, there were those who looked at some simple math and predicted the deaths of hundreds of millions or billions of people. The calculations were simple, multiple crop production by the available land to grow food and then extend population growth forward to a full-fledged 'population bomb' and a massive deficit in food production was inevitable. So of course this would lead to resource wars and worldwide famine. Meanwhile others, most importantly Norman Borlaug, created the green revolution and figured out how to feed MORE people with the available land.
Those who bought into The Population Bomb fell into massive Malthusian logical fallacies and concluded that people were the problem. We had too many people. People were having too many babies. They expressed these concerns in the most outlandish of exaggerated claims. Eventually these chicken little were not only discredited, they became a go-to boogeyman for anyone who didn't like what they were told.
I believe we can always make things better. Most of the major trends of the world from industrialization to trade to immigration to computerization to the internet and social media have made, on balance, the world a better, safer, healthier place. Consuming resources IS the goal. We should be able to have more people, using more electricity and water and eating more meat and so on without destroying the planet. No there is no path to making that work today. Greater resource consumption is directly connected to greater emissions. We just to think smarter about things. I believe we could make a lot more progress on climate change if those on the correct side of it would agree to focus on how to have our cake and eat it too. It is funny how that expression is meant to highlight sacrifice. Similarly, too much of the modern environmental movement focuses on slowing progress, making people poorer and less free. Is that really the best we can do?
Climate change deniers see themselves as identifying this generation's Ehrlichs. Exaggerators who who can't be trusted. Ignore them and they will be proven wrong sooner or later. Meanwhile, some smart guy in a lab somewhere will probably solve the whole thing anyway, they assume. The biggest target are alarmist predictions. Denier sites are littered with quotes from the 1980s through An Inconvenient Truth, released over a decade ago that they say are littered with disproved doomsday scenarios.
This week the world's first commercial carbon capture plant came online. It is 1000 times more efficient than photosynthesis at capturing carbon from the air. Climeworks, the startup that created the facility, doesn't want to be identified as saying this alone will solve the problem. Not even close. But the question is, can't we refocus the climate change discussion away arguments that we need fewer people, living poorer lives and consuming fewer resources. If you believe that together we can do amazing things then let's agree to focus on solutions not prohibition.
This Harvard Business Review article shares some examples of how machine learning is being applied to optimizing business processes. The examples are good ones. 44% of US consumers prefer chatbots to humans. Customer relationships are improved by anticipating behavior and customizing strategies. It is being applied to screening and shortlisting job candidate applications or improving fraud detection and security systems.
The article is a light piece with these and just a few other examples. It doesn't speak to the patterns of these developments. AI is being used as a tool to optimize but it is also doing more and more things better than humans. The impact of AI is such that nearly every organization will need an AI strategy and just as they get one will realize the AI is not something that gets bolted on but is something that gets applied ubiquitous. In other words it becomes everyone's job to eliminate everyone else's job.
This is not to argue that AI should (or could) be slowed or avoided. But it is different than mobile smart devices, social media, online commerce and payments, the internet or even the PC or communications revolutions. AI will eliminate jobs much faster than it will create new ones. The unintended consequences will be huge. Massive shifts in employment could give way to a dramatic overall reduction in the demand for all labor. Those earlier (and still ongoing) technical revolutions did a good job of creating wealth and do create certain types of jobs. Most of my associates and I built careers on that foundation. But AI is not a tool like the others. It is a better way of thinking. Let's hope someone build an AI that can figure this out for us.
Bill Gates and now Mark Zuckerberg have used public forums to talk about the coming seismic shift in jobs that learning machines and automation will have. But I worry what they are actually doing is softening the message for their audiences. Zuckerberg focused in his Harvard commencement address on the impact on low end jobs. Gates sounded his trademark optimistic tone about the future. While I agree that the stats don't lie and we live in an unprecedented period of peace and declining poverty I worry that both of these tech leaders may be sparing their audience a painful message.
Be wary of any conversation about AI and automation that focuses exclusively on self driving cars, warehouse workers and the like. We can deal with those because it implies there will be plenty of opportunity for high paying white collar workers. You can write the whole thing off as an overall positive element of development. From subsistence farmers to information workers in a few centuries. Affected workers can hope to get enough time out of their careers to put their kids through graduate school and look forward to their family moving up.
Instead, what if Zuckerberg stood up at Harvard and said the real risk is to high paying jobs including architects, engineers, lawyers, accountants, managers, doctors and even computer programmers. Presumably, there were few long-haul truck drivers, current or future, in the audience at the Harvard commencement. What if the pair came out and said that programming jobs are at risk? How differently would their audiences respond?
Our economy and society arguably depend upon a few agreed upon premises; social mobility, progress destroys jobs but creates better ones. People need work and will lower standards if forced to. If graduates with JDs cannot find jobs as lawyers and begin competing with business graduates for management or consulting jobs and those people compete with sales people for those jobs where does it end? You cannot run our economy on taxing billionaires alone, in spite of that tiny group's value in political campaigns. The top 1/8th of US households have incomes well above $100k a year. The majority of these people rely on wages, not capital gains or investment income. They are wage earners from dual income sales couples and programmers to dentists, lawyers, doctors and CEOs. Decimate this group and you will have real problems. Imagine the best educated parts of society competing for shrinking job opportunities. What if we create too few robotics engineering and automation design jobs to make up for this. This group represents the upwardly mobile hopes of millions of working class people. Drivers may accept that their job wont always be around so long as their kids can go to medical school. Without that hope and the critical tax base this population represents how can we hope to invest and innovate our way through the job disruptions?
The Verge and others are reporting that Apple's big bets on AI is leading to product innovations sooner than you may expect. This includes their plans to add dedicated AI processing in future versions of the iPhone. The initial use of this is an important one. A lot of needed developments in smart phones are code intensive. This includes augmented reality, biometrics, simulations and advanced encryption technologies. This is not to mention the potential for sci-fi inspired leaps in automated assistants.
Tasks that are very code intensive are therefore processor intensive and that makes them power and therefore battery intensive. Add AI chips to iPhones and you reduce the processing power that needs to be dedicated to these tasks and make smaller batteries last longer.
This is important because productizing AI technologies will drive massive increases in innovations. Profits, manufacturing scale, increased job creation and attraction of investment dollars are the fastest way to accelerate any field. Academic, government and venture investment are important too but the drive they provide is very different. When the field gets pushed forward by the economic tsunamis that products like the iPhone create it will lead to more rapid advances on the other end of the computing spectrum.
Today it can take super computers up to a year to run certain simulations. Quantum chromodynamics is the theory of strong interactions and relates to model of nuclear forces. Astrophysical and cosmological dark matter simulations can take months. So when you buy future iPhones you may be powering the forces that will unlock our understanding of everything from atomic processes to the very structure of the universe.
Last November Microsoft and the University of Cambridge published a paper about how its AI was writing code. The process includes scanning bits of code and considering lots of possibilities for how bits of code could fit together to create the right solution. This was falsely reported as Microsoft AI plagiarizing existing programs. That says something interesting about us and about what learning really is.
We all accept that technological change destroys some jobs and creates new ones. We remain excited about the process because, in general, the newly created jobs tend to be good ones and the innovation lead to positive things for society. As bad as factories were, the small-scale farming people left behind had its issues too. The personal computer revolution created entirely new categories of white collar jobs in the tens of millions. Because of advances in tools and training, it became easier to become a professional computer programmer even without a college degree in computer science. My grandfather turned wrenches, my father was the first in our family to work in an office and I work with computers. One worry about the AI revolution is what if this one doesn’t create new jobs like the past revolutions did?
When you conduct media interviews you get a peak into the process of creating stories. I have seen how journalists find new ideas for new stories in academic and other original sources of news and report on them in industry press. If they are interesting enough then reporters in mainstream press will pick it up. It is often a lot of steps from medical journal to the table in your dentist’s waiting room. It is a complicated process whereby journalists learn about a topic and then simplify it and make it accessible and interesting to their audience. Some reporters do it very well but others get parts wrong. Much like a game of telegraph (or telephone?) reporters will find story ideas from other reporters, often without reading the original source materials. When a reporter misinterprets some coverage and drives an engaging conclusion that can be picked up by another journalist in another story, and so on and so on.
The process of how stories are generated reflects a lot about how humans learn and how original works are created. Even the most novel innovation contains some derivative parts. Solving new problems means building upon what is done before. This is natural and the line between this and plagiarism boils down to now much originality has been added to what already existed.
One of the current fears of AI is that it will replace us in our jobs. One intellectual life preserver for many people in tech is that programming is a creative job that reflects how differently we think than AI ever possibly could. This argument boils down to the view of AI as a tool for automation and not one for actual creation. So if AI steals that means it isn’t creating and programming jobs along with whole categories of jobs are safe. The particularly scary scenario is that AI creates itself and destroys some jobs but does not create new types of jobs. But, AI is already starting to create AI such as this experiment at Google Brain. It is also predicting cancer better than doctors, fighting parking tickets, creating art and stories and yes, writing code. So one of the first things we need to do to prepare for the full impact of AI is to rethink how we think about creativity and learning.
Decide now how you feel about AI powered drones autonomously patrolling the skies. Imagine them learning from successes and failures and using new experiences to make decisions about how to apply rules of engagement to potential to targets below.
Drones have been one of the most controversial new developments in warfare. Your view about a drone in Afghanistan being operated by a military person sitting down the street from a suburban Red Lobster in America is something of a litmus for your views on the use of the military writ large. But autonomous flying vehicles are a separate issue altogether.
A Carnegie Mellon Robotics Institute professor recently let a drone learn how to fly indoors by letting it crash over 11000 times in 20 difference indoor spaces. Watch the video for more.
One of the big questions regarding the future impact and the likelihood of AI doing things we don't want it to is the extent to which its abilities can be extended. I understand the argument that people are prone to making decisions we don't like too. But that alone is not an argument for treading very carefully here.
Read more in this Digital Trends article and tell me what you think.
Microsoft CEO Satya Nadella Keynote at the Microsoft Build event on May 8th 2017 raises important questions about the role that the tech industry has in making sure AI doesn't generate deeply damaging unintended consequences but it also skirts the impact of intended consequences. It also highlights a scary truth, AI is long out of the academic research projects and in the hands of real product development teams. Satya says something I have not seen before. He points out that the future impact of AI depends upon the decisions of individual developers, program managers, business analysts, etc. who make the actual product design decisions behind the software that we use. In my time as a manager in Microsoft's product development organization in Redmond I saw the enormous scale of these software projects. Some are huge but many are small to medium sized teams with autonomy to do really whatever they want. I oversimplify a lot but the important point is that software isn't really a part of some large plan, it is the sum of huge number of individual decisions made by lots of people.
This is a real issue and the big AI houses - including Microsoft, Facebook, Apple, Amazon, IBM - should address this with internal standards and safeguards. Individual responsibility is crucial and creating a culture that cares about the impact of AI and not just the advancement and application of it is crucial as well.
The unintended consequences of AI gone wrong are troubling enough. But we need to really start addressing the intended ones. At the same presentation Satya spoke about the impact on blue collar jobs but that isn't the end of it all. the destruction of millions of driver and warehouse jobs is not the really big problems to solve (although the impact will be huge) but the what happens when large numbers of very broad ranges of high-paying information age and white collar jobs including lawyers, doctors, IT, programming, accounting are made unnecessary but not replaced by new categories of jobs as quickly.
I don't think that AI is going to kill us. I think for starters there are too many smart people worried about that and obvious disincentives in creating a self learning system with that potential. I understand the argument that creating AI is all about creating systems that can develop beyond its programming. The road to killing us is long and filled with enough mistakes that even an arguably fast maturing singularity won't get there.
What I do worry about is the social impact of the effect on the global economy of AI. I am in the camp that believes that few jobs cannot ultimately be eliminated. That won't happen overnight and by then we will have had to deal with a major overhaul of employment as a concept. The issue for the next few decades is that most jobs will be dramatically streamlined with major efficiency improvements. This impacted most fields but we survived that in the big PC, network and internet revolutions from the 1970s to today because it lowered effective costs and these revolutions created entire new categories of jobs, especially high paying ones with trickle down benefits and opportunity. These results didn't trickle all the way and too many people couldn't make the transition leading to a rise in low pay services jobs and underemployment at the same time companies struggled to source enough new tech workers.
AI and Robotics will accelerate with advances from quantum computing. Then, paired with improved technologies in VR and IoT, this will reduce the demand for jobs greater than it creates new ones.
Take legal for example, AI doesn't have to replace lawyers but if it streamlines contracts and advisory services enough as a tool for lawyers than the demand for lawyers is reduced. More formulaic things will automate completely as evidenced by a Stanford student automating traffic ticket defenses. Lawyers are smart but those skills are only so transferable. That makes the supply of legal services relatively inelastic. When the same thing is happening in computer hardware and software, medicine and other major white collar and information age fields, where do these people go?
In this blog I am going to explore these issues and also touch on emerging thinking on how we address these things. This includes the periodic waves of attempts to suppress it like regulations or protectionism but will focus more on solution like reinventing education and guaranteed minimum income solutions.
Mike Zammuto is the CEO of Completed.com and Cloud Commerce and a strategic adviser to several startups. Mike's background is in SaaS services, B2C sites and B2B firms and has worked extensively in online reputation, digital marketing and branding.