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andromedamemes · 5 years

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I recently made a post where I noted the changes that need to be made in electronic systems to make them more respectful of gender identities (especially those transitioning), and I’d thought id share some stuff I know about computers + making things work for all instead of just most people. I’m putting this under the cut because it’s LONG, but I’ve written it so that you don’t have to know much about programming to understand it.

Adding more gender options in an existing system isn’t usually hard. While there are usually a few different things you have to update to get it all working (forms, databases, autogenerated things, etc), the individual changes themselves should be pretty trivial unless the system has been terribly designed, or you’re using some sort of library/software you can’t customize in that way. In other words, if someone is refusing to update an electronic system to accommodate gender identities other than male and female, then either they’re a transphobe (ew), or the system has been designed TERRIBLY. (or the secret third option where they literally do not have the time/money to do that right now or in the foreseeable future). Terrible system designs (when I say system designs here, I’m talking overall about how data is received, processed, stored, managed, and accessed/displayed) go beyond gender. A bad system is one where at least one valid scenario results in some sort of error (like a crash, or the system silently not accepting that input, or producing the wrong answer).

To give you an example of what I mean (for people who don’t know much about programming), consider you must write some simple instructions for testing whether or not it is legal for someone to drink alcohol in Australia, solely based on their age. The legal drinking age is 18 years, so someone is legally allowed to drink alcohol beginning exactly on their 18th birthday. Let’s try writing an instruction using a tiny bit of maths, because computers can understand maths. Instruction: If age > 18 then they can legally drink alcohol, otherwise they cannot. But. This instruction is WRONG. If the person’s age is 18, then age > 18 is FALSE, and our instruction will say that they cannot legally drink alcohol. This is a subtlety in mathematics, that a number cannot be greater than itself. 18 is not greater than 18. However, 18 is “greater than or equal to” 18. So we can rewrite our instructions as so:

Instruction: If age >= 18 then they can legally drink alcohol, otherwise they cannot. Now it works! Great! This sort of thing (someone’s age being 18, just on the edge between legal and not legal) is called an edge case. It is a scenario that is valid and you want your system to work for, but due to some reason (mathematical, or due to language quirks, or conceptually, or just rarity) it can be easy to accidentally leave it out. A significant part of programming is accounting for edge cases. We constantly need to think of all the ways the program could be wrong, and we need to be able to handle every rare but valid scenario, otherwise we have buggy, poor functioning code.

Unfortunately, issues still happen, and they can be very annoying when it affects users. For example, one of the package delivery services in my country flat out does not accept last names with spaces in them when signing for a delivery. My last name has a space in it, and so the system decides to only record half of it when I sign for a package. This also occurs in various other systems, each with their own workaround (like omitting the space). Many many people have spaces in their last name, so how did the programmers forget about them? An even stranger story is that some systems (especially old banking systems) do not accept particular last names, such as Null. In some programming languages, Null means “nothing”, and if the code to process the last name input hasn’t been written properly, then it will see someone enter “Null” as their last name, decide that they actually mean “nothing”, and then tell the user that they inputted their last name incorrectly because a last name can’t be nothing, it has to be something. So people with the last name Null have terrible times with some systems, such as flat out not even being allowed to create an account.

Edge cases are super important, and you are writing bad code if you are not accounting for them. Going back to the gender identity stuff, while people who aren’t strictly cis female or male are rare, they exist. They may be edge cases from a programming perspective (due to rarity), but they are still valid cases. Programmers and system designers should always be considering edge cases, and thus should make it a habit to remember that trans/nonbinary people exist when coding gender related things.

Some programmers will make the argument that trans/nonbinary people are rare and so they shouldn’t bother accommodating their systems for them. But that kind of thinking goes DIRECTLY against how good programmers actually think while programming.

So if a programmer wont take trans/nonbinary people into account, then they’re a bad programmer and a bad human, and that’s the tea.

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smitheeblog · 4 years

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A few thoughts on improving pedagogy

1. We should spend more time creating eureka moments

Learning information doesn’t seem to be linear.

Instead, it seems to come in bursts of “Aha” moments, where lots of information comes together under some grand schema. I remember when I was studying music theory, there was a point where it kind of “clicked,” and all the seemingly unrelated stuff puzzle pieced together.

This seems to be true of most fields. Most of the long-term gains from learning a subject don’t come from being able to regurgitate that information later. Instead, the main gain is the ability to form a mental picture of the concept, that you can slot more information into or draw connections from later.

In that vein, it seems like more education should be focused on cultivating and inspiring these sorts of eureka moments.

E.g I spent a lot of time one semester learning linear algebra, and at the time was pretty competent in doing the very base level problems. But I struggled immensely with the more conceptual stuff, because I didn’t really get it. And I didn’t end up getting ituntil I was able to finally mentally map the relationships between all the component things I had learned. Had the teacher spent 20% less time teaching equations at the beginning and 20% more time trying to get me to understand the big picture, I probably would’ve picked up on the details a lot quicker later

People who are very good at things often make very bad teachers

There are a few reasons for this, I think.

First, people who are very good at things are very removed from the actual experience of learning the thing, so they tend to have forgotten what parts of the thing are difficult to learn and which are easy.

Second, experts also have a very large intuition base, whereas amateurs have very little intuition on the subject, and are usually learning everything from first principles. So, it can be hard for experts to know which things average people have an intuition for, and which things you can only intuit after years of study.

Third, experts usually get to be experts by being very good at their thing at a subconscious level, which often means they can’t explain why they’re making the decisions they’re making.

If you wanted to learn how to write great pop songs, and you went and asked Paul McCartney how to write great pop songs, he would tell you (and this is true) that most of his best melodies came to him in dreams, and being the piano virtuoso he was, he simply woke up in the morning and played what he remembered. Similarly, Steph Curry could give you pointers on shooting three pointers, but he can’t tell really tellyou the secret,because he doesn’t really know the secret. He just shot enough basketballs and they just started going in.

That’s why the best resource for doing your math homework usually isn’t the math teacher, it’s the kid who just did the homework. He knows exactly which problems are hard, exactly what you don’t know how to do, and exactly how to go from “not knowing what to do” to “knowing what to do”

I’m not sure how to implement this idea or make any fundamental change with it, but save for a few subjects that are extraordinarily procedural and memorizable (like cooking), you probably shouldn’t buy a Masterclass subscription.

3. We should think more about what people take away from formal learning

I don’t know about you, but once I learn some sort of subject formally, I usually forget most of the technical nuances unless I’m constantly practicing. But even if I forget it, I think there’s two major benefits to having learned it.

First, it’s really easy to relearn things you once knew. I haven’t taken a physics class in 4 years, but if you gave me the lecture notes from my 11thgrade physics class, I could probably learn a weeks worth of material in about an hour. Muscle memory seems to work in a similar way. Even if they don’t recall it in the moment, it’s remarkably easy for dancers and drummers to recall pieces they once learned

(Most people who’ve taken calculus probably couldn’t solve this right now, but could relearn how to in about 30 seconds)

Second, learning something once improves your ability to gain new information quicker.

This is huge.

I’m going to use computer science as my guiding example for this one, but you can substitute any academic or creative subject you like.

In the times before I knew any computer science, if somebody had given me a bunch of data and told me to dos something to it, the only thing I’d really be able to do is google “how do the function.” Now, even if I’ve never explicitly learned how to do this function, I’ve seen things like it, and I can categorize the type of problem I’m dealing with. Right off the bat, I’ve gained a vocabulary for googling that I didn’t have before.

Now, say I take a crack at it. I expend all of my known tools, only to find that I don’t know how to use one tool in a specific way. But my previous teaching has given me the knowledge to isolate what I don’t know. This skill is massive. In the age of google, it’s foolish to try to teach someone the sum of human knowledge, rather, you get a framework that allows you to learn new information piecemeal as it becomes necessary to know them.

The sum of this is that teachers and instructors should be more aware that this is the primary value they are offering to students. Obviously you have to learn something well once to be able to relearn it later, so rote instruction still has its merits. But more instructors should be focusing on teaching students not only how to master the exact concepts they’re teaching them, but how to go about figuring out which neighboring concepts you need, and how to learn them.

One way to implement this is to make it its own class. For example, to graduate with a computer science degree, you’re required to take this class at the end called “Learning Computer Science on your own” that lays out a map of all of the things that you probably don’t yet know how to do, but that are within grasp of your knowledge, generally what problems those concepts solve, and how to google around for them.

Summed up:

1. It’s more important to have a mental map of concepts than to memorize the concepts. Eureka moments tend to get you there

2. Amateurs are often better teachers

3. Learn to google, teach people to google.

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biopsychs · 7 years

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physics doesn’t have to suck: how to enjoy and do well in your required physics classes

As someone who doesn’t intend to take a physics class ever again, I was relieved when I walked out of my second semester physics final. That said, physics doesn’t have to suck or drag your average down.

(1) How to enjoy physics: Adjust your attitude. Physics is so cool if you actually think about it. Your attitude will dictate your experience. (2) But physics is so hard: Change the way you study and don’t give up. I did better in university physics than in high school. The content was way more difficult but it was my studying methods that made the difference.

This post is split into 3 parts: Introductory physics (very basic physics, that unit of physics you had to do in a lower level science class), high school physics (physics from an algebra-based perspective), and university physics (calculus-based physics and labs). (Obviously these overlap a lot but I needed to organize this somehow)

INFO IS UNDER THE CUT B/C THIS POST IS RIDICULOUSLY LONG

1. INTRODUCTORY PHYSICS

Skills you should master that will greatly help you now and in the future

Converting between units

What all those symbols actually mean

Interpreting what graphs mean

Scientific notation

Know how to do algebra fairly well (esp. rearranging equations)

Khan Academy is a great resource for introductory and high school physics.

Start every question by stating all of your known and unknown variables. Write down which variables you have and which ones you need. Then, you can easily figure out which formula you need.

Make sure you’re actually understanding the concepts behind everything; plugging numbers into equations will only get you so far.

Rearrange formulas to equal the variable you need before you substitute your known values into the equation.

Use your knowledge of physics from your own experiences. Don’t overthink. Just try to picture what would happen if, say, a ball and a feather were dropped from the same height.

2. HIGH SCHOOL PHYSICS (ALGEBRA-BASED)

(Everything from part 1 applies, esp Khan Academy)

Pay attention to in class demos.

Draw free body diagrams whenever you can -- they can be annoying but quickly being able to visualize all of the forces acting is an important skill

Ask your teacher for help or clarification if you need it! You won’t always have the opportunity for one-on-one help, plus your teacher may mark you a bit easier if they see you’re really trying.

Know trigonometry well! In fact, if any of your algebra skills are weak, be sure to review. Don’t let basic math hold you back -- you can do this!

Your first step for any problem should be to write down any known variables or numbers and then the variables you need to find.

Work with a study group (just make sure everyone else is as committed as you are, otherwise studying with others won’t help). People think in different ways and you’re bound to find a solution eventually -- and less likely to give up if you can’t do it.

Get all the part marks. Write down your variables, a formula that could be applicable -- anything that might earn even half a mark (teachers are a lot more forgiving than you think)

Double check your final answer. Ensure you have the right units and ask yourself if your final answer makes sense.

Don’t give up! A big mistake I made in high school was giving up the first time I couldn’t figure out a question because physics was hard and I would never understand it. No excuses! Ignoring a question won’t help you answer it when it comes up on a test. Figure it out on your own or get help.

3. UNIVERSITY PHYSICS (CALCULUS-BASED + LABS)

(Note: Some university physics classes are algebra-based. My university is dumb and forced me to take difficult, calculus-based classes.)

(Again, most things from part 1 and part 2 apply here as well.)

A) Lectures, studying, finals, etc.

Pay attention in class and write good notes

My physics lectures were boring but trying to catch up by reading my textbook later was so much worse

Your lecture notes may not make much sense at first but later on you’ll be able to tell which concepts were stressed by your prof

Draw any diagrams your prof shows you (or take a picture with your phone if you’re lazy). Be sure that the diagram is complete and don’t forget about labels. Don’t worry too much about neatness as long as you know what the diagram is supposed to show you.

Keep all your notes in one notebook: Use one colour for writing regular notes, another colour for circling formulas or starring things you don’t understand, and be sure to write the date down for each lecture and leave space if you fall behind during the lecture (you can always copy someone else’s notes later)

Get a good textbook!

Talk to older students and see if the textbook was helpful for the class. If it’s useful then actually use it! If it’s not, find a good textbook to use!

Do lots of practice questions

My profs tended to go over more conceptual ideas in class and didn’t do many examples.

Try to do a variety of questions! This will tell you if you actually understand the content or if you’ve just memorized how to do certain questions.

Work with other people on assignments (and join/start a group chat for your class)

I had online assignments due every Friday at midnight. My friend and I would meet up on Wednesday or Thursday to work through most of the assignment together. If there was a question we didn’t get, there would always be someone in our class group chat wondering the same thing and there was always some smart physics student that would be a bro and explain how to approach the problem (on another note: don’t leave assignments till the last minute)

Group chats are also great if you miss class or can’t remember when the cutoff for the midterm is

If you don’t understand something get help before it’s too late.

Be prepared with specific questions. It’s hard for someone to help you if all you can say is that you don’t know anything. Go to your prof, TA, tutor, etc.

I found my profs to be super nice about everything. They just want people to be excited about the subject they teach!

If you’re just stuck on one thing there are tons of resources online! Just be specific in what you’re googling and check out resources that other profs have posted online.

Understand the math before you start doing questions

Know the basics of derivatives and integrals

It’s super important to be able to draw a rough graph of the first, second, etc. derivative when all you are given is a graph of the original function (i.e. drawing the graphs for velocity and acceleration when given a graph of displacement)

But don’t ignore the conceptual stuff

This is why a good textbook is important!

Plus you can get part marks for some questions by stating whether one value should be higher/lower than another value, even if you can’t figure out the calculations -- and you can check your answers this way.

For example, it’s pretty important to know what magnetic flux density is before you can calculate it’s value

When studying for tests, don’t just assume you know how to do a question.

Looking over the solution for a problem and actually completing the problem are two very different things. This is the biggest mistake I’ve made when studying physics.

Understanding the solution is only one step in actually being able to answer the question. Looking over solutions is lazy studying if you’re not even trying to do the work. Start the question. Glance at the first part of the solution if you’re stuck. Keep going from there.

For first year physics classes, you really shouldn’t skip over any parts of problem. Yeah, rearranging that formula might look easy but can you actually do it? Practice makes perfect.

If you have a midterm coming up that tests material from a few weeks ago, be sure to do questions from the older units. The content might look familiar but just because you could do a question 2 weeks ago doesn’t mean you can do it now.

Don’t leave your studying till the last minute.

Get a planner and carve out enough time to do practice questions every few days. Trying to catch up on four chapter’s worth of problems is not fun and won’t work very well. Plus, you don’t just have to know how to answer questions. You have to be able to answer questions efficiently.

B) Labs

My labs were very different each semester.

First semester content included kinematics, relativity, forces, momentum, work, etc. The labs were super boring but super easy. For most labs we used motion detectors and a program called logger pro to collect and graph data. Lots of carts.

Second semester content included light, energy, radiation, magnetism, circuits, etc. The labs mostly involved bread boards and wires.

Regardless of content, some general comments on labs are...

Labs won’t always follow lecture content. Apparently that’s too difficult to organize.

That said, get your prelabs done. Properly, if you can. If you don’t fully understand a prelab question, ask your TA once you’ve handed it in. This will save you so much time.

Find a good lab partner. Not sure if there’s a trick to this but just try your best. And be a good lab partner too!

Make note of how strict your TA is with sig figs and error calculations. There’s no sense in losing a few marks when you could stay an extra 15 minutes and do the work properly.

Eat some food and hydrate before your lab -- you never know when your lab will take you 3+ hours to finish.

If you’re not sure if your experiment is working ask your TA. Trying to complete the lab with incorrect data is difficult and your TA will probably make you repeat the experiment anyways.

I hope this post was helpful! I struggled with physics in high school (my worst class) but it ended up being one of my best classes in university (A’s both semesters). The content was way more difficult but my studying habits and test-taking methods were what made the difference!!

Feel free to add additional advice to this post!

My Other Posts:

AP lit tips

high school biology

organization tips

recommended reads

reminders for myself

using your time wisely on public transport

what i learned from high school

#physics does not have to be your enemy!#studyblr #physics #quantumheels #briellestudies #areistotle #focusign #academla #intellectus #studybuzz #etudiance #studypetals #studyblrsubjects #mine

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quickidcard · 7 years

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Artificial Intelligence Bubble vs Reality

Have you heard what they said in the news today or at least recently? The artificial intelligence train has departed already. If you are not on the train you have missed the opportunity or your technology is already outdated. Is it just me or we can hear late nineties World Wide Web bubble 1.0 pumping phase resonating in these statements? Back then the web was supposed to be the next big thing which it actually was except not nearly as big as the size of the speculation investment bubble. That train left, kept speeding up and fell off the cliff leaving many investors with losses that have never been recovered from. Is AI big, bubble or something in between?

Artificial Intelligence concept is much older than the computer age yet no substantial progress has been made those days. The idea of a non-human creation thinking is as old as humankind with the concepts of possessing god powers. Once the matter comes from fantasy closer to more daily practice it proves to be tricky. This brings us to the first fundamental dilemma; do we want to create an artificial counterpart of human brain or rather get a task solved? In which order?

Depending how you look at it the question could be answered in more than one way. While artificial intelligence was established as a separate division of science only in 1956, Alan Turing machine, widely considered as a first computer has its blueprints based on decryption machine that cracked Nazi Germany’s enigma codes back in World War II.

Then came the post WW2 era with a great belief in technology. Rocket science, nuclear explosions development and other achievements in warfare technology during the world conflict set very high expectations for rapid advance. Since it was known by then already that, the brain works using electric impulses the hope was to recreate human mind in the form of an electronic machinery. As it often turns out with high hopes, the reality check result could be pretty hard. Nuclear energy is a great example of this process; over 60 years after the first controlled nuclear reaction we are decommissioning more nuclear power plants than are planned to be built. Nuclear propulsion has never successfully reached beyond military vessels where the cost is never a first factor. Thermonuclear electric power plants remain to be a few years away from being implemented for the past 40 years and a breakthrough does not seem to be in sight. Lunar landing proved to be a great achievement of humanity but also cost prohibitive to be continued at this pace once the military and political goals behind it have been reached. Space shuttle program turned out to be too ambitious and proven deadly when shuttle type turnover time tried to be achieved too aggressively. Was artificial intelligence project fate sealed the same way?

In many ways it actually was but luckily for AI and its enthusiast the patient is far more resilient and hungry for life and the less lucky technology counterparts. One of the more important reasons for that is the fact that no significant progress in rocket or nuclear science can be made in garage setup while substantial advance was secured in these very conditions in the area of computer technology.

Unsurprisingly the focus of 1950s and 60s in the AI field was military oriented. With secured funding and Cold War armament race pressure no potential was to be overlooked. Encouraged by Nazi enigma cracking success the scientist and engineers turned to believe that machine based instant translation from Russian to English was possible. It would have given the US intelligence and military a great advantage over their rival counterparts. While Russian and English have immensely deep differences in grammar and syntax, Noam Chomsky work from the time seemed to be able to help overcome this obstacle. What lied a foundation for the project failure were the common, causal language parts, the phrasal verbs so obvious in every language that many people forget they exist. These disambiguations, while trivial at the human level proved killers for the computers turning the translation job results into jokes and anecdotes fodder. Large funds committed into failed research must have ended the way it did. Spending cuts and many years of promising projects collecting dust on the shelves waiting for funds and talents to pick them up again. In this particular struggle of human against machine the human was a winner but certainly not the men and women behind the project.

We still learn today how difficult this project was. Google Translator, probably the most well-known and one of the most advanced translating solutions is based on the human translation principle. Instead of trying to translate using dictionary it uses a method based on the Rosetta stone. The famous piece of rock was found at the end of 18th century and it contains a decree written in Ancient Egyptian hieroglyphs, Demotic script and Greek. The historians’ community considers it one of the most important piece in learning ancient Egyptian writing. Similarly, the Google translator uses big data containing large amounts of human translated documents to find an accurate counterpart for a phrase in another language and still not without errors.

Many well-funded projects of seventies and eighties of the twentieth century failed in similar way to the instant translator fiasco. I do not want to sound like an armchair general but most of these failures follow the same scenario. Expectations have been set stellar to attract and secure funding. Once the research and development started, it was not long until king was found naked. This naturally caused spending cuts and shorter or longer winters within the subject area. Sounds exactly like a speculation bubble, doesn’t it? Maybe except for funding, public vs private.

It may sound by now I am only looking for the failures and setbacks in the process of developing artificial intelligence but instead I want to point out how difficult and complicated the process be. Citing Galileo Galilei ‘All truths are easy to understand once they are discovered; the point is to discover them.’ It is part of the western culture to believe that once something has been achieved it must have been easy to achieve while the reality is quite the opposite. While we all like to celebrate success, paying attention to the disappointments that led the way to that success might be a better forward looking strategy.

Has anything worth mentioning been achieved then in the area of machine mimicking human in rational thinking process? We have certainly seen impressive progress is selected divisions where the progress was not necessarily expected. Scientist and engineers defeated so many times decided to fight back in the areas where general audience is to be impressed. Beating Garry Kasparov, the world chess champion at the time by the computer was headline news. Sure, the skeptics pointed out that the IBM Deep Blue code was modified during the game but still the computer managed to defeat a champion. Moreover, this was just the beginning.

Chess was always considered a geeky math game with its capacity to predict the largest number of future moves as the main advantage of the winner over the loser. Math is the domain of the computers, they talk math so sooner or later the machine had to be able to outrun humans in this discipline. The real challenge would be a synthetic mind that would be able to compete with humans in their native tongue competitions and winning the TV knowledge show Jeopardy was definitely a desirable trophy.

It was the computer giant IBM to pick up the glove again. The beginnings were not very promising however as the idea sounded too gimmick and resembled failed attempts from the past to imitate human reasoning at the higher level of conceptual thinking. Paul Horn, then director of IBM Research, said the project was in its embryonic stage when he left IBM in 2007 http://www.techrepublic.com/article/ibm-watson-the-inside-story-of-how-the-jeopardy-winning-supercomputer-was-born-and-what-it-wants-to-do-next/. Yet it only took another 4 years for Watson to win Jeopardy against two champions in 2011. That was one impressive growth from an embryo to an adult in just 4 years. How such an impressive progress has been achieved and how the idea was missed so many times in the past? Well, Rosetta stone again …

The language structures human thinking. If it is not difficult enough that each language or at least language family follows different grammar and syntax it’s also the fact that no human used language is a rigid and predictable as math strings. Human language cannot be translated into math accurately and the computers how we are able to design them can only understand math. Extending computer capacity beyond understanding math attempts all failed miserably in the past. What Watson team has done different is realizing computing engine strengths and weaknesses. Synthesized minds as we know them cannot think like humans but they can memorize and sort developed human thoughts without necessarily understanding them. Just as Labrador breed dogs leading the blinds and understanding over 200 voice commands don’t actually need to understand every action they perform. The goal is to execute an action called.

Memorizing a phone book is an impossible task for an ordinary human but rather a trivial job for even simple system like a phone even from before smartphones age. All that needs to be done to win Jeopardy is to memorize commonly available data, index it properly so it is quickly available and have an algorithm to bring it by formulating an appropriate question. Once the stellar dream of mimicking human mind was abandoned and highly selective activity goal was set the task has become achievable. In addition, it only took 4 years.

Still, wouldn’t it be a waste of resources to spend all these years in order to win a TV quiz? Media attention, sure but this could be achieved much cheaper, especially in tabloid journalism era we are in today. Even giants like IBM is unlikely to do it just for the fun of it and sure, they did not. Watson next target is health care and it is already at work in the area of radiology. Careful selection of this branch of medicine again shows strength of the computers in the opposition of the human weakness. What radiologists do is analyze the X-ray images of human body. From a synthetic brain point of view, it is looking for specific patterns in the ocean of dots, a tedious and eye straining activity for a human vision system. In addition, when it comes to how many reference images a person’s mind can store compared to a database is not even fair as the scale is completely different. When doctor Smith, even if he or she is a devoted radiologist might have seen thousands of images and read about another hundreds in specialized literature the image library that Dr. Watson has in its synthetic mind is counted with millions. Does this mean human radiologist profession faces extinction? Certainly not in my opinion. At the end of the day, it is human health and life we talk here; not to mention professional liability that comes with it. The bottom line is that Watson is a radiologist instant access and always updated knowledge base but not a doctor himself. I would be even surprised that the radiologist population shrinks because of Watson advantage. This particular profession is among the best paid in the industry, as the volume of work is immense. With larger throughput, I would rather expect an increase in medical imaging demand as it stays one of the best way to achieve noninvasive diagnostics.

Are we then at the edge of a new breakthrough in technology where nothing will be the same again? With millions of jobs slashed like phone books and more to come once the AI advances. Well, I would not hold my breath for that. All the mishaps mentioned above along with Dr Watson career path show how complicated the process of synthesizing human mind is. Humanoid robots able to pass Turing test seem galaxies away and will likely to occupy fantasy space of fiction rather than walking your home town streets with people unable to identify them without custom id badges. What I expect is a continuation of what works which helping humans where human capacity falls short of the computer one and being a human is certainly not one of these.

from QuickIDCard.com Blog http://www.quickidcard.com/newsroom/artificial-intelligence-bubble-vs-reality/

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