AI: Applied Indirection Part II

In yesterday’s post we told you that many companies that were touting AI were not actually selling Artificial Intelligence or even anything remotely similar (including, but not limited to, Autonomous Intelligence, Augmented Intelligence, Assisted Intelligence, and/or Amplified Intuition) and were, in fact, using the buzz-acronym to accomplish applied indirection and sell you 90s tech in a shiny new wrapper, proffering yesterday’s miracle cure for all of your current woes.

The only difference between the 90s solutions and todays is that today’s look nicer, run faster (but that’s mainly due to the exponential increase in computing power), and have more automation built in. But RPA — Robotic Process Automation — is NOT AI. It’s just using a rules engine and workflow to automate common tasks under typical conditions.

So how do you tell the difference between Applied Indirection and real (WEAK) AI? Well, first you think about what AI means, apply a little common sense, and ask some good questions.

Let’s start with thinking about what AI really means. AI typically stands for Artificial Intelligence, and the definition of AI in its strongest form is machine intelligence, where the machine can acquire knowledge, learn, apply it, and adapt to new, previously un-encountered real world situations in a general manner just as a human would do. If you think about it, no machine can do that, and no machine is even close. So there’s really no such thing as AI (and won’t be for decades).

At the same level of complexity is Autonomous Intelligence, which is Artificial Intelligence that is capable of acting on its own without any human interaction. Since true Artificial Intelligence doesn’t exist, it should be obvious that Autonomous Intelligence (outside of living beings on our planet), which is an AI agent that can work in complete isolation from human interaction, doesn’t exist either.

At the next level down we have Augmented Intelligence, where we don’t define a platform as being intelligent but as capable of providing knowledge and insight that we can use to complement and enhance our intelligence and make as faster and better at the tasks we are performing. At this level, there are tools that exist for well defined tasks, but they are few and far between. While there are a lot of systems that can allow us to do our jobs faster and better, they don’t augment our intelligence. For a system to truly be an augmented intelligence system, it must augment our intelligence, propose actions that we were not aware of (and would not think of in a little bit of time), and make us smarter over time. Very few systems do that, even when limited to very specific tasks.

Going down a level, we have Assisted Intelligence, where we don’t define a platform as intelligent, but capable of using knowledge and insight that it has to complement and enhance our daily performance of tasks by helping us to do them faster, better, or both. Like augmented intelligence platforms, they should be able to prescriptively suggest actions or workflows, but we don’t require that they be capable of identifying anything we wouldn’t in our jobs.

The big difference between augmented and assisted is that a platform that analyzes market data and dynamics and comes up with one of a pre-set of sourcing strategies as a recommendation is generally just assisted intelligence. In comparison, a platform that not only pulls in market feeds but scours the web for public pricing, articles on supply / demand (im)balance, third party audits, and reports on recent events and other data not pushed through integrated feeds; creates multiple pricing and availability projections; runs those projections through multiple models; and then recommends you extend the current agreement and buffer stock three months of supply (because an earthquake in China just closed down the mines that supply a significant amount of the rare earth metals used in your product and supply is likely to become scarce and pricing rise in six weeks) would be considered to be an augmented intelligence platform because even though you could do web searches to find updated public pricing, supply projections, third party audits, and natural disaster reports, there’s no guarantee you’re going to find the report on the local Chinese news feed (that won’t get picked up by an English news feed for two weeks because China downplayed the effect of the earthquake) when you only read English.

In other words, there are some assisted intelligent tools out there (that help you do your job better and faster, but aren’t going to do anything you can’t or come up with anything you wouldn’t if you just spent five minutes thinking about it), a few augmented intelligence platforms for specific tasks, but no autonomously intelligent, artificially intelligent, and definitely no cognitive platforms on the market — and if someone is trying to sell you that, they are using the marketing technique of applied indirection to sell you modern silicon snake oil.

You have been warned!

AI: Applied Indirection

You read that right. AI at most companies is not Artificial Intelligence. It’s not Autonomous Intelligence, Augmented Intelligence, Assisted Intelligence, or even Amplified Intuition. In reality, it is marketers taking Green Day’s AI a little to literally (and treating everyone like an American Idiot*) and repackaging old tech with a new label.

You see, most of what the Marketing Mad Men are trying to sell as AI are just old-school statistical algorithms in a brand-new wrapper. And the only reason these technologies are finally hitting the market and getting good results is the sheer amount of processing power and data we have at our disposal — because dumb algorithms (which is what they are) only work well when you have a lot of processing power, a lot more data, and the power plant to run that processing power 24/7 at 99% capacity across dozens, if not hundreds, of trial parameterizations until you find something that, well, just works.

But it’s not intelligence. It’s advanced curve fitting, regression, k-means clustering, support vector machines, and other statistical inference techniques that existed in SAS in the 1990s. Except now, the curve fitting is nth degree polynomial, advanced trigonometric, geometric, n-dimensional, step-wise, and adaptive. The regression is nonlinear, non-parametric, stepwise, and much more robust … and accurate because you can process millions of data points if you have them. The k-means is not clustering around one or two dimensions, but one or two dozen if necessary in a large multi-dimensional space — and the clusters can be of arbitrary n-dimensional geometric shapes using kernal machines. The support vector machines are not just based on primal, dual, and kernal classification with a bit of gradient descent but enhanced with multi-class support vectors, advanced regression, and transduction (to work with partial valued data). And so on.

And don’t think there’s anything new about “deep neural networks” either. They are just multi-level neural networks which were common-place in the 1990s with more levels and more nodes per level with more advanced statistical classification functions in each node trying to figure out how to extract patterns from unclassified data to classify and structure it, which happen to get better results because they can work on millions of data points, instead of thousands, and do tens of millions of calculations and re-calculations instead of tens of thousands. And that’s the only reason they get better results “out of the box”. There is absolutely nothing better or more advanced about the core technology. Nothing. It’s still as dumb as a door-knob, no matter how whizz-bang the markets make it out to be.

And at the end of the day, the “active” part of the neural network is a fraction of the overall network (which means as much as 90% of the computation is wasted), and if that can be identified and abstracted, you typically end up with a small neural network no bigger than the ones being used twenty years ago, which, even if more than three or four layers, can probably be redesigned as a three-or-four layer network. (See the recent article on the recent MIT Research, for example.) [But if you’ve studied advanced mathematical systems, this is not an unexpected results. Over-dumbification has always led to unnecessary processing and inferior results. Of course, over-smartification also leads to ineffective algorithms because data, typically produced by humans, is not perfect either and we need to account for this as well and detect small perturbations and deal with them. But it’s always better to be thoughtful in our design than to just brute force it.

In other words, many modern marketing madmen in enterprise software have become the new snake-oil salesmen, often selling simple statistical packages for a million dollars or raising tens of millions for yesterday’s tech in a shiny new wrapper. But it’s not intelligent, or even intuitive, by any stretch of the imagination.

That’s not to say that there isn’t technology that can qualify as assisted technology (and maybe even augmented in special cases), just that the majority of what’s being pushed your way isn’t.

So how do you know if you are among the majority being subjected to Applied Indirection or one of the few minority being offered a solution with true Assisted Intelligence capabilities? Stay tuned as we discuss this topic more in depth in the weeks to come …

* It’s much preferable to be a Canadian Idiot. We’re nicer and the “AI” marketers don’t bother us as much.

A Mere Eighty Nine Years Ago Today …

The Tariff Act of 1930, known as the Smoot-Hawley or Hawley-Smoot Tariff, was signed into law and implemented protectionist trade policies that raised tariffs on over 20,000 imported goods to the highest levels since 1828 (when the US relied very heavily on tariffs for public funding purposes).

Why is this act so significant? Most economists agree that this act and the following retaliatory tariffs by America’s trading partners were MAJOR factors that resulted in the reduction of American exports and imports by more than half during the GREAT DEPRESSION.

And let us point out that it wasn’t called the GREAT DEPRESSION as a marketing gimmick. It was called the GREAT DEPRESSION because it was one of the greatest declines of the global economy in all of recorded history! In a mere three years, the worldwide GDP fell by an estimated 15%. That’s FIFTEEN times the decline of GDP during the Great Recession of 2008-2009.

In most countries, this depression lasted until the late 1930’s — a whole decade — and in some countries it lasted all the way until the beginning of World War II! In other words, it was 15 times as bad as the Great Recession (which is a marketing gimmick as this was just a blip when all was said and done) and lasted 15 times as long!

And while it officially stated on Black Tuesday (on October 29, 2009), the Tariff Act of 1930 only served to exacerbate a bad situation, and turned what might have only been a truly great recession into a great depression.

So, my American Readers, tell me what you think is going to happen if you decide to keep your current trade-war happy President in charge for another term (assuming the world survives his first term)? [The worst thing is that his father had to live through the Great Depression and should have not only understood how bad protectionism can backfire but passed that message on as a successful businessman.)

Sourcing Talent Is Rare, Especially Since They Also Have to Manage Risk

Yesterday we told you that Sourcing, like the many facets of Supply Management, is not as easy as it seems as the skills required to go from RFI to award are numerous and compose up a laundry list that is rare to find even in most sourcing teams, yet alone individuals, including:

  • (Cost) Analysis / Market Analysis
  • Logistics
  • Needs Identification
  • Negotiation
  • Project Management
  • Resource Management
  • Supplier Identification
  • Trend Identification
  • … and …
  • Risk Management

The last of which we left off of yesterday’s list because this is a list in itself. You see, in today’s Sourcing landscape, turbulence is not just what you experience in an airplane on your way to a site visit – it’s what you experience trying to manage your supply chains on a daily basis. Just like fluid flows can become highly irregular with the slightest perturbation, so can the flow of goods in today’s ultra-outsourced ulta-global supply chains.

Turbulence is a hidden risk in every supply chain, and one most organizations are never prepared for because, when a risk assessment is done, it is always focussed on easy-to-identify technological, economic, market, financial, organization, environmental and social risks — not random events that can temporarily interrupt your supply chain and cause temporary disruptions with serious financial or brand consequences. Temporary disruptions which, if regular in nature, can put your organization in real jeopardy and temporary disruptions, which, by their very nature cannot be planned for or even identified in an up-front risk assessment.

For example, when buying product components from China, an experienced risk team is going to identify:

  • Supplier Risk
    Are they financially stable? Will they adequately protect your IP? etc.
  • Factory Risk
    Is the quality acceptable? Are there workplace or safety hazards that could shut it down?
  • Port Risk
    Will the product be safe? Is there any danger of strike or overcapacity? On both sides …
  • Export and Import Risk
    Are all regulations adhered to? RoHS? WEEE? Has all the paperwork been completed and submitted on time?
  • Technology Risk
    Is the real-time product tracking and distribution system reliable? Backed Up? Integrated properly with all parties?
  • Environmental
    Is the product being made or stored in areas subject to regular natural disasters such as hurricanes, typhoons, earthquakes, etc.?
  • Social Responsibility
    Is the product conflict / slave labour free? Are all employees of all partners treated equitably? Is the product, and its production, environmentally friendly or at least environmentally safe? Can the product be safely disposed of?
  • Market
    Will the market still want your product when it is available? Is a competitor going to beat you to the market?
  • Economic
    Will the economy maintain or improve? Or will it worsen, leading to reduced demand across the board? What is the job forecast looking like in target markets – job loss in those areas can weaken consumer demand.

and a few dozen other common risks from the risk identification and management playbook.

But it’s not going to identify one-time random events such as:

  • Unlikely Terrorist Attack by a random civilian who goes postal and, when trying to go postal, thanks to a gas leak, accidentally blows up a building due near the docks and causes the port to become unaccessible for 3 days
  • Delayed Delivery due to Paperwork Mix-Up
    One truck is scheduled for delivery of your product to your distribution warehouse, another for mid-term storage at a competitors warehouse on the other side of the continent. And because the small carrier you’re using doesn’t have real-time inventory tracking, and your product is scheduled for JIT delivery, the mix-up isn’t detected until the expected delivery date when your product is half-way across the country.
  • False Stock-Out due to Inventory Mis-Key
    The clerk enters 8,000 units instead of 80,000 into the system, stores exactly 8,000 in the proper location in the ware-house, and puts the other 72,000 units of your hottest selling product at the back of the warehouse reserved for discontinued inventory.

Each of these events can happen, and each can cause a real, unexpected, and unpredictable turbulent impact to your supply chain. Are you ready for it? Can you sourcing team react and adapt when it does?