The Future of Artificial Intelligence: Crash Course AI #20 - Free Educational videos for Students in K-12

The Future of Artificial Intelligence: Crash Course AI #20 - By CrashCourse

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00:0-1	Hi everyone . I'm Jibril and welcome to the final
00:02	episode of Crash course Ai we've covered a lot of
00:06	ground together from the basics of neural networks to game
00:09	playing language modelling in algorithmic bias . We've even experimented
00:13	with code in labs and as we've been learning about
00:16	different parts of artificial intelligence as a field . There
00:19	are a couple of themes that keep coming up .
00:21	1st . Ai is in more places than ever before
00:24	. The machine learning professor Andrew ng says that artificial
00:27	intelligence is the new electricity . This is a pretty
00:31	bold claim but lots of governments are taking it seriously
00:34	and playing to grow education , research and development in
00:37	Ai china's plan alone calls for over 100 billion U
00:40	. S . Dollars in funding over the next 10
00:43	years . Second AI is awesome . It can help
00:46	make our lives easier and sort of gives us superpowers
00:49	, who knows what we can accomplish with the help
00:51	of machine learning and Ai . And third Ai doesn't
00:55	work that well yet . I still can't ask my
00:58	phone or any smart device to do much and we're
01:00	far away from personal robot butlers . So what's next
01:05	? What's the future of Ai ? Yeah . Mhm
01:15	. One way to think about the future of AI
01:18	is to consider milestones AI hasn't reached yet . Current
01:21	soccer robots aren't quite ready to take on human professionals
01:25	and series still has a lot of trouble understanding exactly
01:28	what I'm saying for every AI system , we can
01:31	try and list what abilities would take the current technology
01:34	to the next level . In 2014 , for example
01:37	, the Society of automotive Engineers attempt to do just
01:40	that for self driving cars there to find five levels
01:43	of automation for each additional level . They expected that
01:46	the Ai controlling the car can do more without human
01:49	help . At level one , cruise control automatically accelerates
01:52	and decelerates to keep the car at a constant speed
01:56	. But everything else is on the human driver at
01:58	level three , the car is basically on its own
02:01	, it's driving , monitoring its surroundings , navigating and
02:04	so on . But a human driver will need to
02:06	take over is something goes really wrong , like really
02:09	bad weather or a downed power line . And at
02:12	level five the human driver can just sit back ,
02:15	have a smoothie and watch crash course ai while the
02:17	car takes him to work through rush hour traffic .
02:20	And obviously we don't have cars with the technology to
02:23	do all this yet . But these levels are a
02:25	way to evaluate how far we've come and how far
02:28	research still has to go . We can even think
02:30	about other AI is using levels of automation , like
02:33	for example , maybe we have level one Ai assistants
02:36	right now that can set alarms for us , but
02:38	we still need to double check their work . But
02:41	what are levels two through five , what milestones would
02:44	need to be achieved for an Ai to be as
02:46	good as a human assistant ? What will be milestones
02:49	for computer vision or recommend our systems or any topics
02:52	discussed in this course , We'd love to read your
02:54	ideas in the comments . Sometimes it's useful to think
02:57	about different kinds of ai on their own as we
03:00	make progress on each very difficult problem . But sometimes
03:03	people try and imagine an ultimate Ai for all applications
03:07	and artificial general intelligence or A . G . I
03:11	understand why there's such an emphasis on being general .
03:14	It can be helpful to remember where all this Ai
03:17	stuff first started and for that let's go to the
03:20	thought bubble . Alan turing was a british mathematician who
03:23	helped break german enigma codes during World War two and
03:26	helped define the mathematical theory behind computers in his paper
03:29	computing machinery and intelligence . From 1950 he introduced the
03:33	now famous turing test or the imitation game turing ,
03:37	proposed an adaptation of a guessing game . In his
03:39	version there's an interrogator in one room and a human
03:43	and a machine in the other . The interrogator talks
03:46	to the hidden players and tries to figure out which
03:48	is human and which is a machine turing . Even
03:51	gave a series of talking points like please write me
03:53	a sonnet on the subject of the forth bridge at
03:56	34,957 and 70,764 . Do you play chess ? I
04:03	have K . At K . One and no other
04:05	pieces . You only have K . At K .
04:07	Six . In our at our one it's your move
04:11	. What do you play ? The goal of the
04:13	imitation game ? Was a testing machines . Intelligence about
04:16	any human theme from math to poetry . We wouldn't
04:19	just judge how really robots fake human skin books as
04:23	turning put it . We do not wish to penalize
04:25	the machine for its inability to shine in beauty competitions
04:28	, nor to penalize a man for losing in a
04:31	race against an aero plane . This idea suggests a
04:34	unified goal for Ai , an artificial general intelligence .
04:38	But over the last 70 years AI researchers focus on
04:41	sub fields like computer vision , knowledge representation , economic
04:45	markets , planning and so on . Thanks doc Global
04:49	. And even though we're not sure if an artificial
04:51	general intelligence as possible , many communities are doing interdisciplinary
04:55	research and many air researchers are taking baby steps to
04:59	combine specialized sub fields . This involves projects like teaching
05:02	a robot to understand language or teaching an AI system
05:05	that models the stock market to read the news and
05:08	better understand market fluctuations to be clear . Most of
05:11	Ai is still science fiction . We're nowhere near Blade
05:14	Runner her or any similar movies . Before we get
05:18	too excited about combining everything we've built to achieve A
05:21	. G . I . We should remember that .
05:22	We still don't know how to make specialized A .
05:25	I . S . For most problems . Some sub
05:27	fields are making progress more quickly than others and we're
05:30	seeing A . I . Systems pop up in lots
05:31	of places with awesome potential to understand how A .
05:34	I . Might be able to change our lives .
05:36	Ai professors Yolanda Gil and Bart Selman put together the
05:40	computing research associations AI road map for the next 20
05:43	years . They predict Ai reducing health care costs ,
05:46	personalizing education as celebrating scientific discoveries , helping national defense
05:51	and more part of the reason they expect so much
05:54	progress is that more people than ever , including us
05:57	, are learning how to build A . I .
05:58	Systems . And all these problems have lots of data
06:02	to train new algorithms . It used to be hard
06:04	to collect . Training data , going to libraries to
06:06	copy facts and transcribe books . But now a lot
06:10	of data is already digital . If you wanna know
06:12	what's happening on the other side of the planet ,
06:14	you can download newspapers or grab tweets from the twitter
06:17	. Api interested in hyper local weather prediction . You
06:21	can combine free data from a weather service with local
06:23	weather stations to know when the water your plants .
06:26	And if you feed that data into a robot gardener
06:29	, you could build a fully automated weather knowing plant
06:32	growing food making garden make your communities around the globe
06:36	are combining data ai and cheap hardware to create the
06:39	future and personalized Ai technologies . Well imagining an ai
06:43	human utopia is exciting . We need to be realistic
06:46	to and many industries automation doesn't only enhance human activities
06:51	. It can replace humans entirely . Truck delivery and
06:54	tractor drivers are some of the most common jobs in
06:57	the US as of 2014 . If self driving vehicles
07:01	revolutionized transportation in the near future will all those people
07:05	lose their jobs ? We can't know for sure .
07:07	But google . Prizewinning computer science professor Moshe Vardi points
07:11	out that this is already the trend in some industries
07:14	. For example . U . S . Manufacturing output
07:16	will likely keep rising but manufacturing jobs have been decreasing
07:21	a lot plus computers use energy and that means we're
07:24	not getting any benefits from A . I . For
07:26	free massive amounts of machines running . These algorithms can
07:29	have a substantial carbon footprint on top of that .
07:32	As we've discussed , you have to be pretty careful
07:35	when it comes to trusting AI systems because they often
07:38	end up with all kinds of biases you may not
07:40	want . So we have to consider the benefits of
07:43	massive AI deployment with the costs and the now famous
07:46	story from a few years ago Target figured out a
07:48	woman was pregnant based on her shopping history and they
07:51	sent her maternity coupons but she was still in high
07:54	school so her family saw the mill even though she
07:57	hadn't told them do we want our data being used
08:00	like this and potentially revealing personal details or what about
08:04	the government ? Should it be allowed to track people
08:06	with facial recognition , installing cameras at intersections ? When
08:10	we provide companies with location data from our phones ,
08:12	we could help them build better traffic models so we
08:15	can get to places faster . Cities could improve bus
08:18	routes . But it also means someone is always watching
08:22	you . Ai could also track your friends and family
08:24	where you shopped eight and who you hung out with
08:27	. If statistics have shown that people who leave home
08:29	late at night are more likely to commit a crime
08:31	and a I know that you left even though it's
08:34	just for late night cookie though . Should it call
08:37	the police to watch you ? Just in case .
08:39	So we can go down any number of scary thought
08:42	experiments . And there's a lot to consider when it
08:45	comes to the future of Ai . AI is a
08:47	really new tool and it's great that so many people
08:49	have access to it . But that also means there
08:52	are very few laws of protections about what they can
08:55	and can't do innovations in a . I have awesome
08:58	potential to make positive changes . But there are also
09:01	plenty of risks , especially if the technology advances faster
09:05	than the average person's understanding of it . It's probably
09:07	the most accurate to say that the future is complicated
09:11	and the most important thing we can do is to
09:13	be educated and involved in A . I as the
09:16	field changes which we're doing right now and crash course
09:19	AI labs , we use some of the same machine
09:21	learning technologies that the biggest companies using their products and
09:26	that the universities rely on for cutting edge research .
09:29	So when we see a company or government rolling out
09:31	a new technology , we know what questions to ask
09:35	, Where do they get their data ? Is this
09:37	even a situation where we want a I to help
09:39	humans ? Is this the right tool to use ?
09:42	What privacy are we giving up for this ? Cool
09:45	. New feature . Is anyone auditing this model ?
09:48	Is this A I really doing what the developers hoped
09:51	it would were also hopefully walking away from crash Course
09:53	AI with some basic tools to build different kinds of
09:56	AI from handwriting recognition to recommend . Our systems were
10:00	excited to see what future you decide to build if
10:03	you want to learn more about AI will include more
10:06	free learning resources . In the description . In the
10:08	meantime I've been making some pretty good progress that john
10:11	green pot . Oh john green but yeah john green
10:19	, but tell the audience what is this pizza ?
10:23	See , not just donuts and bagels anymore . I
10:26	want to thank you all for watching Crash Course AI
10:28	and as they say in john green box hometown ,
10:31	Don't forget to be awesome . Crash Course AI is
10:40	produced in association with PBS Digital Studios . If you
10:43	want to help keep crash course free for everyone forever
10:45	, you can join our community on Patreon and if
10:48	you want to keep up to date with my prototyping
10:50	adventures , check out my channel below

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