Data science /

General vs narrow artificial intelligence

April 15 2018/12 min read
  • Anders Arpteg
    Anders ArptegHead of Research

By now, we have all heard about some of the amazing human vs. machine achievements made in recent years, such as the Watson Jeopardy challenge and DeepMind’s AlphaGo win. One of the most common questions (and one of the most frustrating) is when people ask me: Does AI even exist today? Some people claim that the machines are not intelligent at all compared to humans and they are both right and wrong at the same time. Right, because we are still far away from having machines with similar general levels of intelligence as humans, and wrong because, for small and narrow tasks, the machine is winning the battle over humans for an increasing number of tasks.

This article aims to give some clarification to the question if machines really are intelligent today, and more specifically what the difference between general and narrow artificial intelligence is.

What is general and narrow AI?

Most AI applications today are built with a specific purpose in mind, e.g. for playing chess, forecasting the weather, or predicting future sales numbers. This type of AI application is often referred to as “narrow AI”, “special-purpose” or “weak AI.” For this type of application, humans have defined what data to use, which algorithms to use, and how the model should look like. A machine is built and trained so that it can automatically learn from data how to perform a specific task. However, it only learns how to perform that task and nothing else.

For many of these specific tasks, the machine can easily outperform human performance and thus has "superhuman" abilities. Some famous examples include DeepBlue defeating Garry Kasparov at chess in 1997, IBM Watson winning over humans at Jeopardy in 2011, and AlphaGo winning over humans at Go in 2015.

A number of general-purpose applications of AI exist today but they are still far from having human levels of general intelligence. One famous example is when Google DeepMind built a machine that learned to play Atari games. This may not sound like a very important milestone in AI, but it was. In fact, it was such an important scientific achievement that it resulted in a research paper in Nature, which is one of the most cited scientific journals.

The big achievement was not to learn to play an Atari game, but rather the way that it learned how to play. The machine received no information about how each game worked, it only had the pixel input from the screen, the current score, and the valid actions for the game (this video shows how it after only 4 hours was able to learn how to play the game at an amazing level of performance). In this way, a single machine was able to learn how to play a large number of games, in a general way. This is still just in the domain of playing computer games, but it is one small step closer to achieving general intelligence in machines.

How is narrow AI being used?

Narrow AI is already a part of our daily lives e.g. in search engines, voice recognition, and language translation. We believe that the AI we have today can help us solve many of the world’s most challenging problems by augmenting what humans do well, hence allowing us do more, better. This is why we at Peltarion believe that AI technology, especially the latest and greatest type of AI, should be usable and affordable for all companies and organizations, not only the big and powerful technology giants.

Narrow AI can be used in many fields such as medicine, financial trading, retail, marketing, and even creative applications where intelligent machines are working together with humans to e.g. produce music.

Imagine what’s possible when humans and machines work together to solve society’s greatest challenges

Satya Nadella, CEO at Microsoft

Another example from Peltarion, is using narrow AI to help radiologists detect and segment brain tumors. This is a costly and tedious task for humans since a doctor manually has to go through hundreds of brain scan images for one single patient. With AI, machines can instead learn to perform this task automatically, in such a  way that radiologists can increase their efficiency tremendously in building treatment plans for cancer patients.

For more creative applications of AI, novel innovations such as the Google Magenta project and the Wavenet model can enable machines to interpret audio data, understand differences between speakers and even generate speech from a text with the voice of an arbitrary person.

What can we learn from history?

In the 1950 and ‘60s, people believed that intelligent machines would be easy to build and general AI would be “solved” in a matter of years. A famous example is the 1954 Georgetown experiment that was able to partially translate around 60 sentences from Russian into English. They believed that general machine translations would be solved within five to six years. Obviously, this was not the case, and it turned out to be much harder than previously imagined.

Underestimating the difficulties of building intelligent machines led to many research proposals and projects being abandoned during so-called AI winters. There were two major episodes of AI winters, one at the end of the 1970s and another in the end of 1980s. At times, many people believed it would be impossible to ever build intelligent machines.

In recent years, advances in neural network techniques (often referred to as “deep learning”) have been able to significantly improve the accuracy of predictions. Deep learning can not only learn how to make predictions but can also automatically learn how to transform and represent the data before making the predictions. With current state-of-the-art techniques in for example neural machine translation, any sentence between any pair of more than 100 languages can be translated with significantly higher levels of accuracy.

Why will it be different this time?

AI applications have started to move from academia into industry and are yielding significant improvements in both the quality of the service and return on investment. With the widespread dissemination of smartphones, computational power and internet connectivity more services can be provided digitally and the incentives to automate and improve these services are now bigger than ever.

We also see, that many companies are starting to transform their businesses in order to prepare for an AI-first future, where AI will become the natural component of most products and services. In an AI-first future, customers and clients will expect services to have a high level of intelligence already built-in.

Additionally, a number of nationwide initiatives are emerging, where countries are joining forces to maximize the benefits of AI. According to bibliometric data such as a number of publications, US and China are today leading in AI research quantitatively and are also spending billions of dollars to increase AI efforts nationwide. A number of countries in Europe are starting to strategically reinforce their AI initiatives such as France, Germany, UK, Finland, and now also Sweden.

What about the future?

With the high level of narrow intelligence being developed today, all the investments both in academia and industry combined with the fact that AI is increasingly being used in real-world applications today; AI will have a significant impact on our society. As mentioned in the World Economic Forum, “AI is no longer about a machine playing chess. AI is on the streets driving our cars, call centers talking to customers,  drafting and reviewing legal documents with immaculate precision, it is even trading using indices derived from satellite imagery.

It is also important to make use of AI in the most beneficial way. Satya Nadella was correct when saying: “Ultimately, humans and machines will work together — not against one another. Computers may win at games, but imagine what’s possible when human and machine work together to solve society’s greatest challenges like beating disease, ignorance and poverty.”However, as AI starts to become a natural component of more and more products and services, the number of unintended, but more importantly, unwanted effects or behaviors will also start to increase. We believe there need to be rules in place to ensure public safety and the AI industry needs to be proactive in ensuring responsible innovation and ways of working together to find practical solutions to the societal and ethical implications. However, it is equally important that these rules not hamper AI innovation.

It certainly is a very interesting time we are living in today. Hopefully, within our lifetime, we will start seeing machines with high levels of general intelligence working together and empowering humans to solve our greatest challenges.

  • Anders Arpteg

    Anders Arpteg

    Head of Research

    Anders Arpteg has been working with AI for 20 years in both academia and industry and holds a Ph.D. in AI from Linköping University. Previously, he headed up a research group at Spotify and is now heading up the research team at Peltarion. Anders is a member of the AI Innovation of Sweden steering committee, an AI adviser for the Swedish government, a member of the Swedish AI Agenda, Chairman of the Machine Learning Stockholm meetup group and a member of several other advisory boards. 

What do you want to read?

Get the latest AI news

Sign up to receive email updates from Peltarion

What do you want to read?

Get the latest AI news

Sign up to receive email updates from Peltarion

02/ More on Data science