Skip to main content

“Spooky Bots”—Personalised Dialogues with the Deceased

 In 2016, there was a really unusual development of a chatbot: A memorial

bot for a deceased friend. Eugene Kuyna, a bot developer of Russian descent

from Silicon Valley, got the idea after receiving the devastating news about

the casualty Roman Mazurenko. In defiance of all her ethical reservations,

she collected thousands of lines of chats from other relatives and fed them

into a neural network similar to how Amazon’s Alexa or Apple’s Siri were

developed.

The results are both fascinating and scary. Many of Mazurenko’s

friends that spoke to the bot were staggered at the unique expression of

Mazurenko’s, which his bot had perfectly imitated in many places, Even his

humour shines through at times. A friend once wrote to him, for example:

“You are a genius!” and the bot replied quick-wittedly as Mazurenko would

have: “And good-looking!”



Kuyna collected some log files of the chats in order to be able to get an

idea of the outcome. She noticed that the bot listened more than it spoke.

For many of the relatives, the benefit of the bot was therapeutic. They were

thus able to tell it things they had always wanted to say. Many were able to

bid their farewells to him in this way, a fact that would not have been possible without digital avatars. Yet, the effect can also turn into the opposite and

the mourning phase of the relatives can be suppressed and extended.

The unusual and relevant example closely shows the possibilities that are

open to all of us with this technology these days. Yet, the commercial weighing up of costs and benefits is at least just as important as the continuous further development of the technology. We are living in times where each

one of us individually and society as a whole has to give some thought to

a more responsible use of the new technologies to ensure a meaningful and

profitable use. For, as many advantages as AI brings along, as with any kind

of technology, they come with certain risks that are to be identified and

avoided.

Comments

Popular posts from this blog

What is Machine Learning

 The term machine learning (ML) as a part of artificial intelligence is ubiq- uitous nowadays. The term is used for a wide number of various appli- cations and methods that deal with the “generation of knowledge from experience”. The well-known US computer scientist Tom Mitchell defines machine learning as follows: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E (Mitchell 1997). An illustrative example of this would be a chess computer program that improves its performance (P) in playing chess (the task T) by experience (E), by playing as many games as possible (even against itself ) and analysing them (Mitchell 1997). Machine learning is not a fundamentally new approach for machines to generate “knowledge” from experience. Machine learning technology was used to filter out junk e-mails a long time ago. Whilst spam filters that tack- ...

Possible Limitations of AI-Based Bots

 The examples above already show the present-day potential of AI-based bots. At present, these systems are still in an early stage and still have certain limitations and potentials for optimisation. Twitter Bot Tay by Microsoft Most bots at present are reactive service bots. Engagement bots that actively interact with the users as market and brand ambassadors go one step further. The most famous example here is the chatbot Tay by Microsoft. Microsoft removed Tay from the web apologetically within one day. The example shows that the uncontrolled training of bots by the community can lead to fatal consequences. AI systems still have to learn ethical standards. It thus becomes apparent that even bots require a kind of guideline. Like a journalist has to observe editorial guidelines, bots have to observe certain standards. The next generation of AI-based bots must control and create the possible room for communication. IBM Watson has been able to celebrate quite a few respectable resul...

Why Use Machine Learning?

Consider how you would write a spam filter using traditional programming techniques (Figure 1-1): 1. First you would look at what spam typically looks like. You might notice that some words or phrases (such as “4U,” “credit card,” “free,” and “amazing”) tend to come up a lot in the subject. Perhaps you would also notice a few other patterns in the sender’s name, the email’s body, and so on. 2. You would write a detection algorithm for each of the patterns that you noticed, and your program would flag emails as spam if a number of these patterns are detected. 3. You would test your program, and repeat steps 1 and 2 until it is good enough. Since the problem is not trivial, your program will likely become a long list of com‐ plex rules—pretty hard to maintain. In contrast, a spam filter based on Machine Learning techniques automatically learns which words and phrases are good predictors of spam by detecting unusually fre‐ quent patterns of words in the spam examples compared to the ham e...