Admininstrative notes Projects due today at 11:59pm! You can - PowerPoint PPT Presentation

Admininstrative notes • Projects due today at 11:59pm! You can handin something multiple times (just use the “Overwrite Previous” checkbox if you are resubmitting) so don’t be afraid to test handin early! • Take a screenshot and email your TA right away if something happens and you are not able to submit your project on time. • Make sure your project deliverables (just the deliverable, not the reports) do not have your names on it. For example, your research paper shouldn’t have your name on it but your group report should. Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Admininstrative notes • Blind review assignments have been sent to your CS_ID@ugrad.cs.ubc.ca email. If you did not receive it, please email your project TA. Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Admininstrative notes • We are offering 2% on top of your final grade if you present your project to the class on April 6th. We are looking for five teams to present and in the case where more than five teams volunteer, their peer presentation grade will be used to determine who presents. Email Jessica at jhmwong@cs.ubc.ca if you are interested. Everyone who has signed up has been responded to— if you don’t have a response yet, please resend your email. Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Artificial Intelligence Part 4: Will robots take over the world? Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Learning goals • CT Impact: Students will be able to evaluate a job and say whether or not a computer is likely to be able to do that job in the next 20 years • CT Impact: Students will be able to argue whether they believe that AI is a threat using arguments that show an understanding of CT building blocks. Computational Thinking www.ugrad.cs.ubc.ca/~cs100

So…. will computers and robots take over the world? • First, we have to decide what does that even mean? • Let’s start by looking at one thing that’s in the news: jobs Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Quick individual exercise Write down three careers that you’re interested in – for the long run, not just a temporary job. We’re not asking for a deep commitment – just come up with things you’re interested in. Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Chances that a robot will take over your job in the next 20 years Computational Thinking http://www.businessinsider.com/likelihood-of-your-job-being- taken-over-by-robots-2016-8 www.ugrad.cs.ubc.ca/~cs100

Do you want your career to be any of the jobs listed that are > 75% likely to be done by robots? A. Yes B. No Computational Thinking http://www.businessinsider.com/likelihood-of-your-job-being- taken-over-by-robots-2016-8 www.ugrad.cs.ubc.ca/~cs100

Group exercise • Go to http://www.npr.org/sections/money/2015/05/21/4082 34543/will-your-job-be-done-by-a-machine (On lecture page, or search for “NPR robot job”) • Check out at least one job for at least each person in the group and see how likely they are to be taken over by robots (keep track of percentages) Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Clicker question How many jobs that your group wanted were more than 75% likely to be taken over by robot? A. All of them B. More than half, but less than all C. Half D. Less than half, but more than none E. None Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Let’s look at the top job to be automated: Loan Officer. What does a loan officer do? 1. Approve loans within specified limits 2. Meet with applicants to obtain information and answer questions 3. Analyze applicants' financial status, credit, and property evaluations to determine feasibility of granting loans. 4. Explain to customers the different types of loans and their terms 5. Obtain and compile copies of loan applicants' financial information. 6. Review and update credit and loan files. 7. Review loan agreements to ensure that they are complete and accurate according to policy. 8. Compute payment schedules. 9. Stay abreast of new types of loans In a group, list what computers would have to do to automate this job. Divide the list into (a) things computers can do today and (b) things they can’t do yet. Computational Thinking http://job-descriptions.careerplanner.com/Loan- Officers.cfm www.ugrad.cs.ubc.ca/~cs100

Okay, that one’s pretty clear cut. Let’s look a little further down “Taxi drivers and chauffeurs” – 89% chance Obviously, this requires driving. In a group, list what computers have to be able to do in order to drive. Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Driverless cars have come a long way in 15 years https://www.youtube.com/watch?v=TsaES-- OTzM Computational Thinking www.ugrad.cs.ubc.ca/~cs100

But it’s not all about technology Group discussion: how safe would you feel riding in a driverless car? More, less, or the same than in a regular car? A. More safe B. Equally safe C. Less safe Computational Thinking www.ugrad.cs.ubc.ca/~cs100

What about a steering wheel? Group discussion: would having a requirement to have a licenced driver behind a steering wheel make you feel more safe, less safe, or the same? Why? A. More safe B. Equally safe C. Less safe Computational Thinking www.ugrad.cs.ubc.ca/~cs100

What about a steering wheel? Group discussion: would having a requirement to have a licenced driver behind a steering wheel make you feel more safe, less safe, or the same? Why? Computational Thinking www.ugrad.cs.ubc.ca/~cs100

A big problem is liability Consider driverless car accidents. It can be tricky to determine whether the person at fault is the car manufacturer, the software manufacturer, or the car’s owner. Computational Thinking http://www.npr.org/sections/alltechconsidered/2016/09/20/494765472 /regulating-self-driving-cars-for-safety-even-before-theyre-built www.ugrad.cs.ubc.ca/~cs100

A big problem is liability Case study: Who was to blame? In July 2016, a 40 year old man was killed using Tesla’s Autpilot function on the highway when a truck pulled across the road to make a turn. The autopilot (and presumably driver) failed to see the truck and slammed into it. Some additional factors: • The driver was going 74 MPH in a 65 MPH zone. The driver manually set this speed. • The autopilot is in “beta”: Tesla reminds drivers that it is only to supplement a fully-alert driver • The car shut down the motor the instant of the crash • The weather was very sunny • Europe has a law to require a bar on the bottom of trucks that would have likely stopped the accident Computational Thinking http://www.theregister.co.uk/2016/07/28/tesla_autopilot_death_driver_was_speeding/ www.ugrad.cs.ubc.ca/~cs100

A big problem is liability Case study: Who was to blame? In July 2016, a 40 year old man was killed using Tesla’s Autpilot function on the highway when a truck pulled across the road to make a turn. The autopilot (and presumably driver) failed to see the truck and slammed into it. A. The driver B. Tesla C. Other Computational Thinking http://www.theregister.co.uk/2016/07/28/tesla_autopilot_death_driver_was_speeding/ www.ugrad.cs.ubc.ca/~cs100

Another issue: Adversarial attacks "Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they’re like optical illusions for machines.” – Ian Goodfellow and colleagues at OpenAI Computational Thinking https://blog.openai.com/adversarial- www.ugrad.cs.ubc.ca/~cs100 example-research/

Another issue: Adversarial attacks "Adversarial examples have the potential to be dangerous. For example, attackers could target autonomous vehicles by using stickers or paint to create an adversarial stop sign that the vehicle would interpret as a ‘yield’ or other sign” Original image Perturbed image See: Papernot and colleagues: https://arxiv.org/pdf/1602.02697.pdf Computational Thinking https://blog.openai.com/adversarial- www.ugrad.cs.ubc.ca/~cs100 example-research/

That does not, however, mean that the other jobs will not change • Let’s look at my job • In 2011 MOOCs (Massive Open Online Courses) came on the scene and were predicted to take over higher education within a decade Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Group exercise: Have you taken a MOOC? Why or why not? Computational Thinking www.ugrad.cs.ubc.ca/~cs100

What happened to MOOCs? But some changes have persisted: • Online classes • Blended classes • More videos and such for other classes Computational Thinking http://www.chronicle.com/article/MOOCs-Are-Dead- Long-Live/237569?cid=at www.ugrad.cs.ubc.ca/~cs100

Group exercise: How do you think university education will change in the next 20 years? Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Does this mean there won’t be enough jobs? All this has happened before Launderers Agriculture workers Computational Thinking https://www.theguardian.com/business/2015/aug/17/technology- www.ugrad.cs.ubc.ca/~cs100 created-more-jobs-than-destroyed-140-years-data-census

So what happened to all those people? What are they doing? Accountants Hairdressers Computational Thinking www.ugrad.cs.ubc.ca/~cs100