Interview 1

AI applications, machine learning, human-machine interaction

The following interview snippets were given by Pierre Lison and cover various topics such as the main applications of AI in our society, machine learning and its relations with AI and big data, deep learning, human-machine interaction and its goals as well as the trends and challenges of human-machine interaction.

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The interviewee, Pierre Lison is a senior researcher at a Norwegian computing centre, which is a research institute. They are working on artificial intelligence (AI), statistical modelling and in general computer sciences. The interviewee’s field of research is AI, language technologies and natural language processing.

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True or false?

In the expert’s opinion AI applications are already broadly used in our society and in our daily life.

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True or false?

Search engines, Google, computer vision, voice/speech recognition, object/face recognition, language technology are examples of AI technologies and AI applications.

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True or false?

Recognition of objects in pictures and video screens is not part of this large field called computer vision.

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True or false?

Computer vision is about training a model to recognise patterns such as physical objects, human voice and faces, visual, audio or video input.

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True or false? According to the expert AI is not growing at a fast pace.

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True or false?

Big Data analytics is one of the emerging technology trends that allows the processing of very large amounts of data.

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True or false?

Researchers don’t consider the ethical issues behind AI technologies as important.

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True or false? In the expert’s opinion AI has no influence in our daily routine, nor in our society.

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Machine learning is:

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Machine learning is about: (2 right answers)

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Machine learning: (2 right answers)

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Yes
No
 

Deep learning is the only machine learning technology.

The goal of deep learning is to train neural networks with many processing layers.

Systems can learn how to perform complicated tasks thanks to these many nodes combined and connected in a large network of such nodes.

Deep learning has been used in machine translation which is automated translation.

In machine translation like Google Translate, you don’t need to understand the context of the sentences, the linguistic properties of both languages or how words can combine with each other.

Deep neural machine translation works better and is of higher quality than traditional machine translation, but still needs to improve a lot.

Machine learning is a very general term for everything in AI that is trained from data.

Machine learning does not include deep learning.

Deep learning is a very specific technology based on neural networks.

Traditional programming is what you do when you pre-program the rules in advance and don’t train the models.

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1/3: Where is machine learning being used nowadays?

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1/3: Where is machine learning being used nowadays?

In machine translation, search engines like google search, computer vision, speech recognition, automated decision making, classifications and predictions, robotics, autonomous cars.

2/3: What is the main advantage of machine learning for society?

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2/3: What is the main advantage of machine learning for society?

Automation of repetitive and monotonous tasks and processes.

3/3: What is machine learning based on?

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3/3: What is machine learning based on?

Historical data in supervised machine learning, learning from what we’ve seen in the past and generalising from it.

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1/3: What is the main goal of human machine interaction?

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1/3: What is the main goal of human machine interaction?

Create good interfaces between a human who wishes to perform a particular task and a machine that can perform this task.

2/3: What examples of human machine interaction are there?

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2/3: What examples of human machine interaction are there?

A graphical user interface, talking robots, interaction with technologies.

3/3: What quality do tools/devices/systems/machines need to be like?

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3/3: What quality do tools/devices/systems/machines need to be like?

intuitive enough and transparent

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One important emerging trend of machine learning is to have machines that can adapt to human users, speak their language. Humans spend most of their time talking and listening to each other in spoken language. The future of human machine interaction is to have systems that can interact with us in a way that is familiar to us, either through language or visual effects. The challenge of language technology is the different access to technologies depending on the country and the language.

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