Moravec’s Paradox in Artificial Intelligence: Implications for the future of work and skills

What is artificial in AI?

As the four-day long weekend loomed and I was closing an executive education programme where the focus was digitalization and technology, especially in the context of India and emerging economies, I read this piece on AI ethics by IIMB alumnus Dayasindhu. He talks about the differences between teleological and deontological perspectives of AI and ethics. It got me thinking on technological unemployment (unemployment caused by the firms’ adoption of technologies such as AI and Robotics). For those of you interested in a little bit of history, read this piece (also by Dayasindhu) on how India (especially Indian banking industry) had adopted technology.

In my classes on digital transformation, I introduce the potential of Artificial Intelligence (AI) and its implications on work and skills. My students (in India and Germany) and Executive Education participants would remember these discussions. One of my favourite conversations have been about what kinds of jobs will get disrupted thanks to AI and robotics. I argue that, contrary to popular wisdom, we would have robots washing our clothes, much earlier than those folding the laundry. While washing clothes is a simple operation (for robots), folding laundry requires a very complex calculation of identifying different clothes of irregular shapes, fabric and weight (Read more here). And that, most robots we have are single use – made for a specific purpose, as compared to a human arm, that is truly multi-purpose (Read more here). Yes, there have been great advancements on these two fronts, but the challenge still remains – AI has progressed far more in certain skills that seem very complex for humans, whereas robots struggle to perform certain tasks that seem very easy to humans, like riding a bicycle (which a four-year old child can possibly do with relative ease). The explanation lies in the Moravec’s Paradox. Hans Moravec and others had articulated this in the 1980s!

What is Moravec’s Paradox?

“It is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility”.

Moravec, 1988

It is very difficult to reverse engineer certain human skills that are unconscious. It is easier to reverse engineer motor processes (think factory automation), cognitive skills (think big data analytics), or routinised computations (think predictive/ prescriptive algorithms).

“In general, we’re less aware of what our minds do best…. We’re more aware of simple processes that don’t work well than of complex ones that work flawlessly”.

Minsky, 1986

Moravec’s paradox proposes that this distinction has its roots in evolution. As a species, we have spent millions of years in selection, mutation, and retention of specific skills that has allowed us to survive and succeed in this world. Some examples of such skills include learning a language, sensory-motor skills like riding a bicycle, and drawing basic art.

What are the challenges?

Based on my reading and experience, I envisage five major challenges for AI and robotics in the days to come.

One, artificially intelligent machines need to be trained to learn languages. Yes, there have been great advances in natural language processing (NLP) that have contributed to voice recognition and responses. However, there are still gaps in how machines interpret sarcasm and figures of speech. Couple of years ago, a man tweeted to an airline about his misplaced luggage in a sarcastic tone, and the customer service bot responded with thanks, much to the amusement of many social media users. NLP involves the ability to read, decipher, understand and make sense of natural language. Codifying grammar in complex languages like English, accentuated by differences in accent can make deciphering spoken language difficult for machines. Add to it, contextually significant figures of speech and idioms – what do you expect computers to understand when you say, “the old man down the street kicked the bucket”?

Two, apart from communication, machine to man communication is tricky. We can industrial “pick-and-place” robots in industrial contexts; can we have “give-and-take” robots in customer service settings? Imagine a food serving robot in a fine dining restaurant … how do we train the robot to read the moods and suggest the right cuisine and music to suit the occasion? Most of the robots that we have as I write this exhibit puppy-like behaviour, a far cry from naturally intelligent human beings. Humans need friendliness, understanding, and empathy in their social interactions, which are very complex to programme.

Three, there have been a lot of advances in environmental awareness and responses. Self-navigation and communication has significantly improved thanks to technologies like Simultaneously Localisation and Mapping (or SLAM), we are able to visually and sensorily improve augmented reality (AR) experiences. Still, the risks of having human beings in the midst of a robot swarm is fraught with a variety of risks. Not just that different robots need to sense and respond to the location and movement of other robots, they need to respond to “unpredictable” movements and responses of humans. When presented with a danger, different humans respond differently based on their psychologies and personalities, most often, shaped from a series of prior experiences and perceived self-efficacies. Robots still find it difficult to sense, characterise, and respond to such interactions. Today’s social robots are designed for short interactions with humans, not learning social and moral norms leading to sustained long term relationships.

Four, developing multi-functional robots that can develop reasoning. Reasoning is ability to interpret something in a logical way in order to form a conclusion or judgment. For instance, it is easy for a robot to pick up a screwdriver from a bin, but quite something else to be able to pick it up in the right orientation and be able to use it appropriately. It needs to be programmed to realise when the tool is held in the wrong orientation and be able to self-correct it to the right orientation for optimal use.

Five, even when we can train the robot with a variety of sensors to develop logical reasoning through detailed pattern-evaluations and algorithms, it would be difficult to train it to make judgments. For instance, to make up what is good or evil. Check out MIT’s Moral Machine here. Apart from developing the morality in the machine, how can we programme it to be not just consistent in behaviour; but remain fair and use appropriate criteria for decision-making. Imagine a table-cleaning robot that knows where to leave the cloth when someone rings the doorbell. It needs to be programmed to understand when to stop an activity and when to start another. Given the variety of contexts humans engage with on a daily basis, what they learn naturally will surely take complex programming.

Data privacy, security and accountability

Add to all these, issues around data privacy and security. Given that we need to provide the robot and AI systems with enough data about humans and we have limited ability to programme the system, issues about privacy is critical. Consent is the key word in privacy, but when we are driving in the midst of an autonomous vehicle (AV), there is so much data the AV collects to navigate, we need strong governance and accountability. When an AV is involved in an accident with a pedestrian, who is accountable – the emergency driver in the AV; the programmer of the AV; the manufacturer of the vehicle; any of the hardware manufacturers, like the camera/ sensors that did not do their jobs properly; or the cloud service provider which did not respond soon enough for the AV to save lives? Such questions are pertinent and are too important to relegate to a later date when they occur, post facto.

AI induced technological unemployment

At the end of all these conversations, when I look around me, I see three kinds of jobs being lost to technological change: a) low-end white collared jobs, like accountants and clerks; b) low-skilled data analysts, like the ones at a pathology interpreting a clinical report or a law-apprentice doing contract reviews; and c) hazardous-monotonous or random-exceptional work, like monitoring a volcano’s activity or seismic measurements for earthquakes.

The traditional blue-collared jobs like factory workers, bus conductors/ drivers, repair and refurbishment mechanics, capital machinery installation, agricultural field workers, and housekeeping staff would take a long time to be lost to AI/ robotics. Primarily because these jobs are heavily unpredictable, secondly as these jobs involve significant judgment and reasoning, and thirdly because the costs of automating these jobs would possibly far outweigh the benefits (due to low labor costs and high coordination costs). Not all blue-collared jobs are safe, though. Take for instance staff at warehouses – with pick and place robots, automatic forklifts, and technologies like RFID sensors, a lot of jobs could be lost. Especially, when quick response is the source of competitive advantage in the warehousing operations, automation will greatly reduce errors and increase reliability or operations.

As Brian Reese wrote in the book, The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity, “order takers at fast food places may be replaced by machines, but the people who clean-up the restaurant at night won’t be. The jobs that automation affects will be spread throughout the wage spectrum.”

In summary, in order to understand the nature and quantity of technological unemployment (job losses due to AI and robotics), we need to ask three questions – is the task codifiable? (on an average, tasks that the human race have learnt in the past few decades are the easiest to codify); is it possible to reverse-engineer it? (can we get to break the task into smaller tasks); and does the task lend itself to a series of decision rules? (can we create a comprehensive set of decision rules, that could be programmed into neat decision trees or matrices). If you answered in the affirmative (yes) to these questions with reference to your job/ task, go learn something new and look for another job!


© 2019. R Srinivasan, IIM Bangalore

Will you look for jobs in Facebook?

It has been a wonderful week so far with my lectures on Platform Business Models at the University of Rome Tor Vergata over the past two days. In one of the discussions, there was a discussion about network mobilisation, and a perceptive participant quipped about how successful Facebook can be in a variety of businesses. I have been maintaining that Facebook and LinkedIn are in independent markets, with their own unique needs, and therefore would never end up competing. However, this discussion on what Facebook can do with the big, small and thick data it has about users – ads, shopping, or even jobs set me thinking.

Winner takes all markets

One of the most common discussions on platform and networked businesses is the prevalence of monopolies, in what we call as “winner-takes-all” markets. There are three conditions for these markets to satisfy to qualify as “winner-takes-all” markets – multi-homing costs should be very high; network effects should be strong and positive; and users usually do not have any special preferences (read more about it here). Social networking (with peers, friends, and family) is a winner-takes-all business by all counts – it is difficult to affiliate yourself with multiple platforms; network effects are strong and positive; and Facebook is used for pretty much everything – no special preferences.

Professional networking space, on the other hand, would have different economics. Multi-homing costs are sure high, but not so high. Especially when people have multiple identities … for instance a CEO by the day and a triathlon by the evening; or a professor of law and counsel at the same time. And they could possibly have separate professional networks, right for each of their interests, right. On top of this, online media provide us with our own masks, that enable us to insulate the two worlds when we choose to or integrate when it suits us. A sort of maskenfrieheit, a German word that translates to “masks provided to us by the power of anonymity”. Most of us surely live in multiple worlds, leveraging our own maskenfreiheits. Network effects are sure strong and positive, and in addition to social networking, professional networking business also has a significant extent of cross-side network effects (from potential employers and followers). There are special preferences in professional networking – there are those wo write for others to follow; some others just read and follow and minimally engage (a occasional like here and a share there); and there are few INfluencers (as LinkedIn calls them). So, it makes logical sense that a professional networking business is not a winner-takes-all business, and should be prepared to be attacked by a variety of competitors.

LinkedIn, for its part has done it bit, I would say. It has significantly expanded its reach to college students; allowed for writing (competing with blogs); jobs (competing with focused recruitment sites); shares, likes, and comments (competing with social networking, including micro-blogging). And its merger with Microsoft recently would hopefully provide it more teeth to bite in.

Facebook enters the jobs market

But, how does LinkedIn compete when the ubiquitous Facebook decides to enter the jobs market? I recently read this report on TechCrunch (read it here) on how Facebook is entering the jobs market. With its size of members’ network at more than thrice that of LinkedIn, Facebook can unearth more and more passive job seekers. Those of you who are not actively seeking a job, but would be interested in testing something out, if is offers great roles, salaries, titles, locations, or just more fun that your current role. In fact, the value proposition of LinkedIn was just that – one keeps building a stack of endorsements and a network that will then actively seek you out, rather than the job seeker reaching out. Facebook seems to have imitated just that – its profile tags is much the same as LinkedIn endorsements. Everyone sees the similarity … read the Fortune Business report of July 2015 here.

Is the professional networking space contestable?

Firms competing across business lines can also be explained using the theory of contestable markets. The simplest definition and explanation of contestable markets I could find online is on this page. These markets are characterised by low barriers to entry (like no economies of scale) and low barriers to exit (like no sunk costs), and therefore allow for new entrants to adopt a hit and run strategy. Incumbents typically protect their turf using asymmetric information (some specific information/ competence) that the new entrants do not possess. If we were to look at professional networking space as a contestable market, then LinkedIn had it all covered as an incumbent. Facebook anyway had a variety of small and medium businesses maintaining pages to connect with its customers; and all it had to do was to extend the same feature to job applicants connecting with the firms. Much like how a firm would announce a new product or a discount offer, it could advertise jobs on its Facebook page. Just that Facebook is trying to overcome the asymmetric information bit with its Profile Tags feature to quickly imitate LinkedIn’s endorsements (it is not available in all countries, yet). Without that Facebook would not be able to customise the feed to its readers – you would get only “relevant” job offers on your Facebook timeline, now that it would have your Profile Tags.

Facebook jobs, anyone?

So, would you apply for jobs using Facebook? I for one know a lot of active seekers and college students invest in building their LinkedIn profiles, rather than “wasting time” on Facebook. Facebook is for casual chit-chat with friends and family, sharing selfies, religious views, political statements, and even late-night party stories. Not the place where I would imagine a lot of people would apply for jobs. Will you let your maskenfreiheit down?

But hang on, what about those who do not have a LinkedIn profile? What about those who are logged on to Facebook for ever on their smart phones? What about those who use Facebook to gather information about jobs and then apply for the same using traditional job sites, just email, or through their LinkedIn profiles? Small and Medium businesses might be able to attract a lot of undifferentiated talent (I’m not talking about blue collared workers only) through Facebook, if this succeeds. And what do dedicated job sites like do?

Facebook surely has big data, small data (or thick data) and even the right data (after my posts of the last two weeks, this HBR post on right data appeared online!). Exciting times ahead.


(c) 2016. Srinivasan R.