All The Hype Is About AI, But The Real Action Is In IA

WHAT’S OLD IS NEW

Many AI and machine learning algorithms used today have been around for decades. Advanced robots, autonomous vehicles, and UAVs have been used by defense agencies for nearly half a century. The first virtual reality prototypes were developed in the 1960s. Yet, as of late 2016, not a day goes by when a main stream publication doesn’t pontificate on the upcoming societal impacts of AI. According to CB Insights data, funding for startups leveraging AI will reach $4.2B in 2016, up over 8x from just four years back.

What has Changed?

There are many factors at work, but there is consensus that many recent developments, such as massive recent improvements in Google translate, Google DeepMind’s victory at the game of Go, the natural conversational interface of Amazon’s Alexa, and Tesla’s auto-pilot feature, have all been propelled by advances in machine learning, more specifically deep learning neural networks, a sub field of AI. The theory behind deep learning has existed for decades, but it started to see renewed focus and a significantly accelerated rate of progress starting around the year 2010. 

What we are seeing today are the beginnings of a snowball effect in the impact of deep learning across use cases and industries.

Exponentially greater availability of data, cloud economics at scale, sustained advances in hardware capabilities (including GPUs running machine learning workloads), omnipresent connectivity, and low power device capabilities, along with iterative improvements in algorithmic learning techniques have all played a part in making deep learning practicable and effective in many day-to-day situations. Deep learning, along with other related techniques in statistical analysis, predictive analytics, and natural language processing, are already beginning to get seamlessly embedded in our day-to-day life, and across the enterprise.

MACHINES AND HUMANS

Machines have long been significantly better than the human brain at several types of tasks, especially those that relate to scale and speed of computation. Three academic economists (Ajay A. et al) in a recent paper and HBR article posit that the recent advances in machine learning can be classified as advances in machine “prediction”:

“What is happening under the hood is that the machine uses information from past images of apples to predict whether the current image contains an apple. Why use the word ‘predict’? Prediction uses information you have to generate information you do not have. Machine learning uses data collected from sensors, images, videos, typed notes, or anything else that can be represented in bits. This is information you have. It uses this information to fill in missing information, to recognize objects, and to predict what will happen next. This is information you do not have. In other words, machine learning is a prediction technology.”

Completing any major task involves several components – namely data gathering, prediction, judgement, and action. Humans still significantly outpace machines at judgement-oriented tasks (widely defined), and Ajay et al postulate that the value of these tasks will increase as cost of prediction goes down with machine learning.

While there are purpose-built machines that can demonstrate selected human-like soft skills, machine capabilities in those areas have not made nearly as much progress as they have in the “prediction” function, driven by deep learning, over the past few years. Here are some areas that humans excel at, and new technology breakthroughs may be required for machines to emulate:

  • Learning to learn: Most recent spectacular results from use of machine learning have been generated by machines that observe how humans act in a variety of instantiations of the task at hand (or large sets of inputs and outputs to the problem at hand), and “learn” using the deep neural network approach. Such machines are designed deliberately by a team of humans for a specific task, carefully generating the requisite training data and fine tuning requisite learning algorithms over a period of time. Machine learning techniques today could require orders of magnitude more training data than a human might for performing a similar task, e.g. a toddler can recognise an elephant after seeing a picture of one a few times, but a machine requires a much larger (and possibly primed) data set. Humans are good at learning to learn – they can learn a new skill completely unrelated to their current skill set, can decide what to learn and find and gather data accordingly, can learn implicitly/subconsciously, can learn from a variety of instruction formats, and can ask relevant questions to enhance their learning. Machines today are only beginning to learn to learn.
  • Common sense: Humans are good at exercising “common sense,” i.e. judgement in universal ways without thinking expansively or requiring large data sets. Machines are in their relative infancy in this field, in spite of rapid strides in Natural Language Processing using deep learning. Scientists working on common sense reasoning reckon that additional new advances are required for machines to exhibit common sense. We (or our kids) have all faced this issue while trying to chat with Alexa or Siri.
  • Intuition and Zeroing in: The human brain is good at exercising intuition and zeroing in, i.e. finding a fact, idea, or course of action from a very large, complex, ambiguous set of options. There are ongoing academic efforts and headways on bringing intuition to machines, but machine intelligence is generally very early on this dimension.
  • Creativity: While there are now machines which have generated works of music or art that are indistinguishable to lay persons from the work of masters, these have been largely based on learning the creativity patterns of those masters. True creativity would entail the ability to generate novel solutions to problems not previously seen or to create truly innovative works of art.
  • Empathy: ability to understand emotions, value system, setting a vision, leadership, and other soft capabilities remain uniquely human skills.
  • Versatility: The same person can perform reasonably at many tasks, e.g. pick up a box, drive a car to work, console a kid, and give a speech. Machines and robots are still purpose built for specific tasks.

IA AND AI

Summarising the above – while machines have made rapid strides at learning or “prediction” skills, they are in their early days of attempting to emulate truly “human” skills. We propose that we classify prediction, first-order machine learning, and human-in-the-loop automation capabilities as “IA” technologies. These technologies typically use the unique power of machines (ability to process huge data sets) to effectively augment human capabilities, and the final output of the system typically depends on humans that bring complementary skills, those that train them, and those that design them.

Given its original roots, as well as the potential for confusion with AGI, the term AI should be reserved for machines that additionally demonstrate aforementioned human capabilities of judgement, common sense, innate creativity, ability to learn to learn, and empathy. This may still be short of the omnipotent AGI, but full automation of complex work flows would require machines that have most of these skills.

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