Is AI Ready to Compete With the Human Workforce?

When I first got involved with Artificial Intelligence I was a young and somewhat naive Machine Translation researcher in the early 1980’s. At that time, AI was already going through its second round of big expectations.

The first time coincided with the rise of the digital computer in the 1950’s and 1960’s. This second time was triggered by the arrival of the PC and the early forms of what is now the Internet. Both time the expectations had been high. It was believed we would have computers performing many tasks previously thought possible only for human beings. Computers would hold conversations in natural language, equal or out-perform us in cognitive tasks ranging from playing games like chess and checkers to diagnosing illnesses and predicting market movements.

Both times, the hyped-up expectations ended in disappointment. In the 1990s we did spin off some of our research into actual business applications. We put expert systems in to help process insurance applications, for instance, and a neural net to detect early signs of infection in cattle. But nothing came close to what had been promised and like it had in the 1960’s AI was relegated back to the relative obscurity of academic research and niche applications.

We are currently seeing the third wave of rising hope and expectations around AI, based largely on the convergence of Big Data and seemingly unlimited computing power. There is no denying that progress has been made in many of the fields related to AI. But will this third wave be it? Will this be the moment AI breaks through the cognitive barriers and become the long-promised general intelligence to dramatically change the way we use machines?

Underestimated Hurdles

It’s not that I don’t see the potential of AI and ML. Having successfully implemented such systems in the past I know they can already bring specific benefits to very specific situations. But the hype is not helping anyone making decisions about what to do and don’t do with AI. And that can be really dangerous (see: https://www.technologyreview.com/s/612072/artificial-intelligence-is-often-overhypedand-heres-why-thats-dangerous/). Leading researchers in the field have written not only about the power but also the limits of deep learning and AI’s lack of common sense (see: https://www.youtube.com/watch?v=0tEhw5t6rhc, https://arxiv.org/pdf/1801.00631.pdf and medium.com/@GaryMarcus/in-defense-of-skepticism-about-deep-learning-6e8bfd5ae0f1). For AI to truly deserve the moniker “intelligence” and be as versatile, adaptive and resilient as the human workforce they are often claimed to compete against, both the technologies involved and the way we deploy them still need to overcome several major hurdles. My next three posts will explore three of those hurdles that I feel are vastly underestimated by the current generation of AI researchers and engineers:

  1. The Complexity Hurdle
  2. The Emotional Hurdle
  3. The Socio-Economic Hurdle

Enterprise AI Assumption 1

Length: 4-5 minutes

This is the first of a series of more than a dozen key assumptions we urge you to adopt.

Key assumption number 1: Most enterprises do not need an AI strategy. They need a business strategy and enough technical investment to determine where emerging technologies (like AI) can have a significant impact on existing and potentially new business strategies.

In Architects of Intelligence, writer and futurist Martin Ford interviewed 23 of the most prominent men and women who are working in AI today. Every one noted limitations of AI systems and key skills they were still trying to master. One, Stuart Russell, Computer Science professor at the University of California, Berkeley told the story of the invention of nuclear chain reactions.

The consensus view as expressed by Ernest Rutherford on September 11th, 1933, was that it would never be possible to extract atomic energy from atoms. So, his prediction was ‘never,’ but what turned out to be the case was that the next morning Leo Szilard read Rutherford’s speech, became annoyed by it, and invented a nuclear chain reaction mediated by neutrons! Rutherford’s prediction was ‘never’ and the truth was about 16 hours later.

http://book.mfordfuture.com/

What a wonderful introduction to defining AI!

AI is an endeavor to simulate (or surpass) the intelligence of people without really understanding the essence of human intelligence. (Which is OK as a premise but let’s not fool ourselves into thinking we have any idea of how to really do this.)

It’s Impossible!

Most AI research has its roots in finding things that people do which machines cannot do and many believe will be impossible for machines for the foreseeable future. (Doing the impossible also includes doing things thought impossible for both people and machines.)

The effort around creating Amazing Innovation that defeats the Always Impossible is usually near the edges of the current known science.

These Amazing Innovations get heralded as Artificial Intelligence for a while, but then we realize there are more mountains to climb. This one is no longer Amazing since we now know someone has found a viable way to do it. Hence, Amazing Innovation deteriorates into Aging Innovation.

AI is a continuous innovation process built around creativity and upsetting conventional wisdom and establishing new standards of excellence, all of which is washed away as people come to treat it as ordinary.

AI is already here. And not here. Every major cycle follows this three stage pattern:

  • Always Impossible (AI)
  • Amazing Innovation (AI)
  • Aging Innovation (AI)

Aging Out of Amazing

When we look at all the technologies that have gone through the three stage AI cycle, we find many that were at their peak of Amazingness many years ago but now they’re no longer thought of as AI. Examples include:

  • Rule-based systems
  • Expert systems
  • Simple statistical machine learning
  • Simple robotic process automation (screen scraping, scripting and automatic text re-entry

Aging Innovations have their place in a technology tool kit, but none of them are examples of modern, high impact Amazing-Innovation AI technologies.

Today

We see AI everyday in our smartphones. We interact with it via Alexa, Siri and Google Assistant. AI:

  • Subtly nudges and explicitly guides our on-line experiences
  • Shapes our social interactions
  • Influences social and cultural norms and our votes in elections.
  • Driving more and more of the behavior of our autos, as well as devices in our homes and in offices

Most of these experiences are consumer-level experiences. But some have already traversed the boundaries into the enterprise and more are on the way. These tools are highly imperfect but nonetheless useful. They include

  • Corporate search engines (and their more focused models such as legal e-discovery tools)
  • Automated speech transcription, translation and summarization tools
  • Help desk chatbots

Enterprise focused consulting firms, services providers, technology vendors and others are generating white papers, surveys and special reports telling us that:

  • It’s time to AI or die. (And Digital or die, Blockchain or die and soon, Quantum or die.)
  • We are behind the leading edge unless we’ve already developed an AI strategy and are executing forthwith (in partial fulfillment of our Digital strategy and so on.)

But the reality of what we see and hear from enterprise clients is different. Many have made large investments in substantial AI projects, only to back away when the results failed to match the hype. Most organizations are hiring, training, experimenting, piloting, building prototypes and waiting for more evidence of clear, uncontentious business value before going full bore with major AI-based projects. 

The market for AI has not crossed the chasm. It’s still early stage.

Why? Compelling new business strategies exploiting capabilities only available via AI are missing.  Yes, some enterprises are focused on AI as their primary and key competitive advantage.  They’re in technology. For them, AI research and development is a central part of their business strategy.

For most other enterprises, AI is making commercial progress in practical (if sometimes limited) applications that deliver clear business results. AI technology may be essential to the strategy but they differentiate from others in their industries based on other values. 

  • There’s a lot of spending on chatbots, for example. They can cut operating costs. Under some conditions, they can also raise customer satisfaction.
  • Visual recognition systems (beyond user authentication) are beginning to attract a lot of early-stage attention in industries as diverse as agriculture, security and transportation. Customers in these spaces are not interested in becoming “AI Firms”. They want to improve crop yields, interdict evil-doers and match your luggage, face and seat assignment on airplanes.  

Key assumption number 1: Most enterprises do not need an AI strategy. They need a business strategy and enough technical investment to determine where emerging technologies (like AI) can have a significant impact on existing and potentially new business strategies.

Author Disclosure

I am the author of this article and it expresses my own opinions. I have no vested interest in any of the products, firms or institutions mentioned in this post. Nor does the Analyst Syndicate. This is not a sponsored post. 

5 Easy Criteria To Get Quick Returns on AI Investments

Reading Time: 4 minutes.

Minimum investment, maximum return and pretty fast too.

Based on research below, we intend to adopt these findings ASAP for the Syndicate. While there are some very positive use cases, continue to cast a skeptical eye on much of the breathless business value chatter about AI today.

Context

We have seen many major AI research breakthroughs deliver significant new technical capabilities this decade. And more are coming. But most published market surveys inaccurately predict rapid, broad-spread enterprise technology adoption. That’s wishful thinking. These surveys are cognitively-biased exercises in deception. The authors deceive themselves. Most predictions of widespread adoption are self-serving. And wrong. We’ve been finding:

Most enterprise AI activity has not passed beyond the serious play stage. It’s confined to:

  • Experimentation and technical training
  • Pilot projects (that fail to achieve production use)
  • Reuse of older analytical tools and methods disguised as AI breakthroughs

Virtual agents and virtual assistants account for the largest single enterprise investment area. These uses have merit. But users and sponsors are underwhelmed by the end results. Implementers soldier on because they are delivering cost reductions that sustain management interest.

Shining Lights

There are a few shining lights to analyze. Research from the NBER (National Bureau of Economic Research) is one such standout. But who has the skills, time and energy to read academic research? Us for starters, at the Analysts Syndicate. We’ll translate.

One of my (many) academic favorites is Erik Brynjolfsson (of MIT and NBER). Erik and his coauthors have published many valuable peer-reviewed papers on the business impacts of technology adoption.

His central AI related finding?

He can’t find any evidence of a significant economic impact of AI adoption thus far.

In Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics Brynjolfsson, Rock and Syverson (2017) say that

AI’s full effects won’t be realized until waves of complementary innovations are developed and implemented.

And it may take many decades or longer to develop those waves of innovation.

Look at other breakthrough technologies (which economists are now labelling ‘general purpose technologies’) such as:

  • Electricity
  • Steam Engines
  • Heavier-than-air aircraft
  • Gasoline engines
  • Chips (semiconductor devices)

The uses for electricity, steam, airfoils, gasoline and chips continue to evolve. Commercial use of electricity has been with us for a century and a half. New uses for electricity and electricity-powered devices continue to emerge. Likewise for other general purpose technologies.

How much evolution is enough? (It’s easy to say in hindsight but that really only tells us if we’ve waited too long.) How will we know when there has been enough complimentary innovation to say the core breakthrough technology is now ready for large scale deployment and exploitation?

A clever answer

Brynjolfsson, Hui and Liu turned the question around. They looked for AI technologies and use cases where there didn’t seem to be any major needs for complimentary innovations. (They also wanted a use case and technology pair that wouldn’t require business process, organizational or cultural changes.) They settled on testing Automatic Machine Translation (AMT) in eCommerce.

In Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform these researchers report on the impact of applying AMT on eBay’s web commerce platform and found large, statistically significant improvements in international trade sales volumes from 17 to 20 percent.

Wake Up Call

Some new AI technologies (like AMT) can deliver benefits quickly and at minimal cost.

Seek opportunities that do not require significant business process, organization, technical or culture change.

Five Easy Criteria to Seek

  1. Fast time to implement. Can a viable production instance be up and running in 90 to 120 days? (Avoid massive reengineering projects of all types.)
  2. Low levels of internal disruption and reinvention. Let the results drive disruptive change instead of requiring disruptive change to achieve the results.
  3. Suppliers and service providers with the right business mode. (If a significant part of their business is time and materials consulting, you may be failing points 1 and 2 and taking on unjustified risk.)
  4. Relevant real world experience. (Demand verifiable references – use cases – that are already in volume production. Visit them and dig deeply.)
  5. Revenue enhancement (which beats cost reduction.)

You can fail all these tests and still succeed. But you can succeed more quickly, with lower cost and risk, if your project passes all these tests. Succeed quickly and then iterate.

Net Recommendations

  • Apply AMT to your e-commerce initiatives to almost painlessly increase sales and expand available markets.
  • Apply the Five Easy Tests before making strategic AI investment decisions.
  • Read at least section 1 of Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform
  • Respond back to this posting (either via public comment or private communication) with (a) your own examples that conform to the 5 easy tests, and (b) additional easy tests you would apply to make the list stronger.

Disclosure: I am the author of this article and it expresses my own opinions. This is not a sponsored post. I have no vested interest in any of the firms or institutions mentioned in this post. Nor does the Analyst Syndicate.