week 7 [09.01-15.01.2017] Crowdsourcing.

Hello everyone.
This week is my time to indulge you. Here is a subject for all of you..

The subject is probably well known by all of you. Personally I am interest in this field of work and I would like to know your point of view on this kind of work.

The Crowdsourcing.
Small general information about the crowdsourcing by Jeff Howe.

The second part of my post is the actual issues to discuss.
I give you one of interesting ways to use CS in biotech science and here is a small TED talk by Ellen Jorgensen, one of pioneers of DIY bio.



Direct link to the site article >>

This film is only a sample of specific use of the idea of Crowdsourcing. I would like to encourage you to use some imagination and try to predict the ways it could be use.
The Crowdsourcing subject is very wide and I am aware that a lot o people are against that kind of work assuming they will loose their jobs. I ask you to avoid that kind of assumptions and try to focus not on prejudice but on benefits.
My questions for a current discussions are:

1. What is the limit of the crowdsourcing work?
2. What is the problem with security in place where everybody know all about each other?
3. Could CS be the future way of work? where there is no private companies but only private ideas?

Thank you all for watching this and answer some questions.

week 7 [09.01-15.01.2017] The wonderful and terrifying implications of computers that can learn.

Hi Everyone,

I would like to share a wonderful TED Talk video by Jeremy Howard,  an entrepreneur, business strategist, developer, and educator. In addition to beeing an accomplished businessman, he is a researcher at the Universit of San Fransisco and previously worked as Chief Scientist at Kaggle.

Although this is one of the many talks popularizing deep learning and its applications it is nevertheless worth watching. If only because of entertaining examples of machine learning systems.

1a) Do you think that people doing white collar jobs face the same fate as blue collar workers? Is fear of automation justified or is it just a hype?

1b) What kind of jobs do you think will be automated next? What should be automated and what shouldn't? Do you see any safe niches or potential business models that could emerge?

2) Do you think that we should adjust our economic structures to reflect the new reality? What changes could/should we implement?

3) What other cool applications of deep learning have you seen? Care to share?

Traditional Classes

Dear Students,

Decide when you would like to deliver your presentation.
It should be from the area of your studies and it should be 10-13 minutes long.

1st year students meet with me on 27 January at 5 p.m. (obligatory classes).

27.01.17 5 -8 p. m. 

5.00 -6.30 p.m . 1 Year Students 

Presentations (1^st Year Students who are not from Warsaw)

1.  6.45 p.m.

2.  7 p.m.

3. 7.15 p.m.

4. 7.30 p.m.

30.01.17 5-8 p. m. 2^nd and 3^rd Year Students

5.00 -6.30 p.m.  obligatory classes for the weaker students

Presentations

1.  6.45 p.m.

2.  7 p.m.

3. 7.15 p.m.

4. 7.30 p.m.

31.01.17 5-8 p. m.  All PhD Students (delivering presentations)

1.  5 p.m.

2.  5.15 p.m.

3.  5.30 p.m.

4.  5.45 p.m.

5.  6 p.m.

6.  6.15 p.m.

7.  6.30 p.m.

8.  6.45 p.m.

9.  7 p.m.

10. 7.15 p.m.

01.02.17  5-8 p. m  All PhD Students (delivering presentations)

1.  5 p.m.

2.  5.15 p.m.

3.  5.30 p.m.

4.  5.45 p.m.

5.  6 p.m.

6.  6.15 p.m.

7.  6.30 p.m.

8.  6.45 p.m.

9.  7 p.m.

10. 7.15 p.m.

Week 7 [9 - 15 January] Bacteriophages to the rescue

Hi Everyone,

I decided to present the following scientific article on tackling the problem of drug-resistant bacterias (commonly known as superbugs):

http://nautil.us/issue/43/heroes/will-viruses-save-us-from-superbugs

This is a nice read.

To cut a long story short it talks about (hopeful) renaissance of well known viruses that kill bacterias, especially the antibiotics resistant ones. In order to prevent from being killed the bacteria has to evolve, and by mutating it is  losing a drug resistance becoming vulnerable to antibiotics.

There are questions that pop up in my mind and I would love to see your answers to them:


1. Can you think of ways to increase the speed of finding matching viruses (the ones that would kill the bacteria)? These ways don't have to be technical.

2. One obvious way would be to use computers to aid finding the right bacteriophage. In your opinion what kind of software would handle this task best?

3. If your decide on using Machine Learning, what algorithms or architecture you think would be the most promising?


Thanks and Best Regards,
Paweł.

Week 7 [09.01-15.01-2017] Some like it hot - visual guidance for preference prediction

Hi everyone and all the best in the new year!

It is this time of the season that numerous resolutions emerge and a lot of people decide to start being the better version of themselves - gyms are full, diets start, enrolments for educational courses plummet. This week I decided to suggest an article from the researchers who have developed an app which predicts age and attractiveness level. Their work showed significant results in predicting ratings on previously unseen photos uploaded to a popular dating website.

https://arxiv.org/abs/1510.07867

As a discussion starter I would like to ask your opinion on the following questions:

1. What are the biggest obstacles for preference prediction algorithms ? 
2. In your opinion, should attractiveness be measured? Alternatively, does it cause any major ethical problems?
3. Is one photo enough to measure attractiveness? What other features researchers might use?
4. What are the possible applications of age, gender and attractiveness prediction algorithms?