Self-study|IT|Int|Lesson 7: Machine and deep learning

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Read the statements and mark them as True or False


💡Machine learning is a modern innovation that has helped people enhance not only many industrial and professional processes but also advanced everyday living. So today we are going to focus on its applications in different industries and then go over its limitations.

Ready? Let’s get started!

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Do the quiz and find out how much you know about machine learning


Complete some facts about machine learning


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Scan the text and mark the sentences as True or False

Glossary

  • an adjustment — a small change
  • to determine — to discover the facts or truth about something
  • an artificial neural network — a set of algorithms, modelled after the human brain, that is designed to recognize patterns

Machine learning vs. deep learning

Machine learning and deep learning have garnered a lot of attention over the past two years. Machine learning is algorithms that analyze data, learn from that data, and then apply what they’ve learned to make informed decisions. A simple example of a machine learning algorithm is an on-demand music streaming service. For the service to make a decision about which new songs or artists to recommend to a listener, machine learning algorithms associate the listener’s preferences with other listeners who have a similar taste in music.

Deep learning is a subcategory of machine learning. Technically, it is machine learning and functions in a similar way, but its capabilities are different. While basic machine learning models do become progressively better at whatever their function is, they still need some guidance. If an AI algorithm returns an inaccurate prediction, then an engineer has to step in and make adjustments. With a deep learning model, an algorithm can determine on its own if a prediction is accurate or not through its layered structure of algorithms called an artificial neural network. So a deep learning model is designed to continually analyze data with a logic structure similar to how a human would draw conclusions.

A great example of deep learning is Google’s AlphaGo. Google created a computer program with its own neural network that learned to play the abstract board game called Go, which is known for requiring sharp intellect and intuition. By playing against professional Go players, AlphaGo’s deep learning model learned how to play at a level never seen before in artificial intelligence, and did without being told when it should make a specific move.


Read the text once again and recap the differences between machine and deep learning

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Look at the list of industries that are facing some problems and suggest how machine learning could improve them:


Listen to how machine learning helped solve real-world problems and choose one industry for each of them. There is one extra industry

1. A recent groundbreaking discovery of new genes responsible for the Amyotrophic Lateral Sclerosis disease was made at Barrow Neurological Institute in partnership with the artificial intelligence company IBM Watson Health. IBM Watson, the artificial intelligence computer, reviewed thousands of pieces of research and was able to identify new genes linked to the disease.

2. According to the report by Stanford University, not only will self-driving cars reduce traffic-related deaths and injuries, but they could bring about changes in our lifestyles as well. We will have more time to work or entertain ourselves during commutes.

3. Last year, students at Georgia Institute of Technology in the US were startled to discover that their helpful teaching assistant had in fact been a robot all along. After initial teething problems, the robot started answering the students’ questions with 97% certainty.

4. Google has used its artificial intelligence platform DeepMind to predict when its data centres will get too hot. Since cooling systems are only activated when required, AI has saved Google around 40% in energy costs at its server farms.


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Listen to the audio one more time and then explain how machine learning helped solve the four problems

1. A recent groundbreaking discovery of new genes responsible for the Amyotrophic Lateral Sclerosis disease was made at Barrow Neurological Institute in partnership with the artificial intelligence company IBM Watson Health. IBM Watson, the artificial intelligence computer, reviewed thousands of pieces of research and was able to identify new genes linked to the disease.

2. According to the report by Stanford University, not only will self-driving cars reduce traffic-related deaths and injuries, but they could bring about changes in our lifestyles as well. We will have one more time to work or entertain ourselves during commutes.

3. Last year, students at Georgia Institute of Technology in the US were startled to discover that their helpful teaching assistant had in fact been a robot all along. After initial teething problems, the robot started answering the students’ questions with 97% certainty.

4. Google has used its artificial intelligence platform DeepMind to predict when its data centres will get too hot. Since cooling systems are only activated when required, AI has saved Google around 40% in energy costs at its server farms.



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Think over these questions and share your opinion:

1. Have you heard of any other problems that have been solved using machine/deep learning? Give details.

2. Do you believe AI systems can solve any problem? Why (not)?

3. What are some problems that AI systems won’t be able to solve?

Use the voice recorder.

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Listen to the audio and mark the sentences as True or False

The hysteria about the future of artificial intelligence is everywhere. Just looking at the media headlines, you might think that we are already living in a future where AI has infiltrated every aspect of society. While it is undeniable that AI has opened up a wealth of promising opportunities, we need to be aware of its limitations and where humans still need to take the lead. Instead of painting an unrealistic picture of the power of AI, it is important to take a step back and separate the actual technological capabilities of AI from magic.

We now see governments pledge support to national AI initiatives and approach start-ups to provide AI solutions for the public sector. But the problem is that AI systems need a lot of data to function, and the public sector typically does not have the appropriate data infrastructure to support advanced machine learning. Most of the data remains stored in offline archives, and the few digitised sources of data that exist tend to be buried in bureaucracy. More often than not, data is spread across different government departments that each require special permissions to be accessed.

For a long time, Facebook believed that problems like the spread of misinformation and hate speech could be algorithmically identified and stopped. But under recent pressure from legislators, the company quickly pledged to replace its algorithms with an army of over 10,000 human reviewers.

The medical profession has also recognised that AI cannot be considered a solution to all problems. The IBM Watson for Oncology program was a piece of AI that was meant to help doctors treat cancer. Even though it was developed to deliver the best recommendations, human experts found it difficult to trust the machine. As a result, the AI program was abandoned in most hospitals where it was trialled.

These examples demonstrate that there is no AI solution for everything. Using AI simply for the sake of AI may not always be productive or useful. Not every problem is best addressed by applying machine intelligence to it.



Read the task and talk about some limitations of machine learning

Listen to the audio again and take some notes about machine learning limitations. Then, use your notes to prepare a speech and describe a few limitations in different industries. Provide some examples to support your arguments. You can include both the examples given in the audio and your own examples. Finally, you can express your own opinion on the topic.

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Complete the collocations with the given words


Read the task and suggest possible solutions to the mentioned problems. Use the phrases from the first exercise and useful language below

Imagine that you have decided to take part in a 🔗Power Workshop Day at ML Conference, where people are challenged to solve real-world problems. Each participant has to choose one serious problem of their city or country and imagine how it can be solved by implementing machine learning.

Taking into account all the uses and limitations of machine learning we discussed earlier, think of one problem and suggest how to deal with it. Use the text area below to make some notes.

Useful language

  • It would be a good idea to…
  • Taking all of the factors into account…
  • One suggestion could be…

Justifying

  • The reason I believe that is…
  • The facts suggest…
  • Taking into account what I have seen/read…

Expressing certainty

  • From my own personal experience, I am lead to believe…
  • There are many facts in favour of my opinion. For example, …

Conclusion

  • To sum up…
  • In a nutshell…
  • All things considered…

Use the voice recorder.

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Read the text comparing machine learning and deep learning and complete the gaps with the missing words and phrases


Read the article about three types of machine learning and do the tasks

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Machine Learning

There are three major recognized categories of machine learning: supervised learning, unsupervised learning, and reinforcement learning.


Scan the text excerpts again and mark the sentences as True or False

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Listen to a simple introduction to natural language processing and match the beginnings of the sentences to their endings

Natural Language Processing, usually shortened as NLP, is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of human languages in a manner that is valuable. Most NLP techniques rely on machine learning to derive meaning from human languages.

Natural Language Processing is the driving force behind many applications: language translation applications such as Google Translate; word processors such as Microsoft Word and Grammarly; personal assistant applications, such as OK Google, Siri, Cortana, and Alexa.

The main challenges we face in NLP today are related to the imprecise characteristics and ambiguity of natural languages, which makes the process of understanding and manipulating them extremely complex. As a result, it makes a natural language processing system difficult to be implemented.


1. implement NLP systems. 3. such as language translation applications, word processors and personal assistant applications.
2. read, decipher, understand, and make sense of the human languages. 4. interact with humans using the natural language.

Урок Homework Курс
  • True or False?
  • Machine learning
  • Deep and machine learning
  • Machine learning uses
  • Benefits of AI
  • What AI can't solve
  • Problems to solve
  • Homework 1
  • Homework 2