It is important to understand what Pepper the robot is not and what she cannot contribute, Mike Mullane explains.
There is a scene in James Cameron’s original Terminator movie where a flophouse manager pounds on the killer cyborg’s door. Before telling the manager to get lost, the Terminator scans a list of possible answers. Pepper the robot’s widely reported testimony to British MPs earlier this week brings the scene to mind because it illustrates the difference between artificial intelligence and pre-programmed responses.
For anyone who doesn’t know, Pepper is a semi-humanoid robot manufactured by the Franco-Japanese Softbank Robotics. Pepper has earned a minor footnote in history by appearing before a British Parliamentary select committee to testify about artificial intelligence (AI). That is like asking a burger flipper to comment on the Michelin Guide.
As the MIT Technology Review reports, Pepper’s testimony has upset a lot of people in the AI research community. Many have dismissed the event as a publicity stunt and worry that it will mislead the public about the current state of the technology.
The way the story was reported also tells us a great deal about the state of technology journalism in the mainstream media. For instance, a respected British newspaper revealed an alarming lack of knowledge by speculating on whether Pepper is pre-programmed to answer questions or if it relies on AI.
Here’s the thing: Pepper cannot actually think for herself (itself?) and only operates in highly controlled environments. She only managed to cope because both questions and answers had been pre-arranged. One day Skynet may come, but for the time being machines are incapable of imitating human intelligence.
The experts differentiate between ‘strong AI’ and ‘weak AI’. Strong AI, sometimes called general AI, refers to a machine able to solve any problem requiring advanced cognitive abilities. It would be able to deal with new situations and solve problems it has never faced before.
Neither Pepper nor apparently T-101 possesses this kind of AI. They have weak AI, also known as ‘narrow AI’, which supports humans in solving problems in specific use cases.
AlphaGo, for instance, thrashed the human world champion of the board game Go, but would be useless at anything else. Virtual assistant like Alexa and Siri combine different weak AIs to create a kind of hybrid intelligence.
Virtual assistants can search the internet for basic information, schedule events and reminders and operate smart devices, among other things. This is not general intelligence.
‘Machine learning’ and ‘deep learning’ are two frequently used and often misunderstood terms.
In machine learning, the machine builds up the knowledge to complete specific actions based on training data covering multiple datasets. There are many examples of machine learning in our daily lives.
The performance of machine learning algorithms is directly related to the available information, which is referred to as ‘representation’. A representation consists of all the features that are available to a given machine and selecting the right representation is highly complex.
Representation learning is a field of machine learning that exploits raw data to automate the task of selecting the right representation. This is dependent on various environmental factors and far from simple.
For example, it can be difficult to distinguish colours in low light. Snowflakes, seagulls and shadows from trees are just a few of the things that can baffle self-driving vehicles.
Deep learning is a subcategory of representation learning that can transform features and elaborates dependencies based on inputs received. When a deep learning machine sees a picture, for example, it will map the pixels to the edges, to the corners and finally to the contours in order to identify an object.
In other words, AI is not a single technology but a collection of technologies. This should not detract from the very real achievements of the brilliant people who built Pepper. It is important, though, to understand what she is not and what she cannot contribute.🔷
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(This piece was originally published on the PMP Blog!)