How artificial intelligence is changing our society

00:00 artificial intelligence is a bit like a
00:02 human who is inside something else it’s
00:12 not as smart as you but it could be as
00:14 smart as you in the future I believe
00:20 that we’ll become robots at some point
00:22 too
00:36 [Music]
00:42 artificial intelligence is changing our
00:45 lives but what can it really do what
00:49 will change and what will remain science
00:52 fiction to answer these questions we
00:57 embarked on a journey to meet the
00:59 scientists working on our future
01:02 [Music]
01:06 Augsburg in southern Germany home to the
01:09 headquarters of cuca the world’s leading
01:12 manufacturer of industrial robots vana
01:17 Bischoff is head of research here and is
01:20 considered to be one of the world’s
01:21 leading experts in this field he and his
01:26 team are working on a new generation of
01:28 robots that learn independently like
01:30 children the task to recognize and sort
01:34 building blocks this robotic system
01:47 taught itself how to grab in other words
01:49 there was no human programming the robot
01:52 so we tried by himself he tried by
01:57 himself like a child when he first
01:59 started grabbing he wasn’t successful
02:01 except for him one to two percent of
02:03 cases but he observed himself and by
02:06 observing himself the robot identified
02:08 when an image successfully matched a
02:10 particular grasping motion and when it
02:12 didn’t he’s applied what he learned and
02:17 now he can successfully grab these
02:19 objects over 90% of the time I didn’t
02:23 program him and yet he’s still learning
02:24 the task by himself seeing that really
02:26 motivates you to stand on Susan
02:31 but what if the robot sees a new object
02:33 such as pliers it’s a nice example every
02:39 child would just say okay grab and move
02:42 those pliers over no problem
02:43 but he’s still failing he’s failing
02:48 because he doesn’t know what kind of
02:49 inertial force this object has to be
02:51 able to grab it properly but you can see
02:57 how he’s already trying out different
02:58 methods and in time he may get the hang
03:00 of it I should add that he isn’t trained
03:02 after every attempt he collects around a
03:04 thousand data and then the neural net is
03:07 retrained so it’s possible that if we
03:09 let him try a thousand times now he
03:11 would at least be able to grab them
03:12 reliably intelligent robots that learn
03:17 by themselves they can recognize parts
03:20 assemble them and they can independently
03:22 adapt to their environment with the help
03:24 of AI
03:31 but we’re only in the early development
03:34 stages I have a favorite example and
03:39 that’s chess these days there are
03:41 computers or AI that can beat chess
03:43 champions I thought we don’t have a
03:47 robot that can reach into a bookcase
03:48 take out a chess set open the box take
03:51 out the pieces one by one set them up
03:54 and start playing the six-year-old can
03:58 do that but no existing robot can so for
04:01 the moment whenever I need physical
04:02 intelligence we’re still doomed to fail
04:04 and I think that will be the case for a
04:06 few more years yet machines are getting
04:14 better and more intelligent this video
04:17 was produced using special effects but
04:22 this robot has learned how to play table
04:24 tennis he was built by researchers and
04:27 tubing him and shows how much is already
04:29 possible in the real world how long will
04:33 it take before robots are better than us
04:34 in some areas yeah enjoyed and funeral
04:37 robots already are better than us in
04:39 many areas particularly those requiring
04:41 non variant repetition a great deal of
04:43 force or a high degree of precision
04:47 current robots are not as good at as we
04:50 are are those involving sensors there’s
04:52 no point denying that and I think it
04:54 will be another 10 or 20 years before we
04:56 have robots that can hold a candle to
04:57 humans in some areas
05:02 we humans use all of our senses and can
05:05 do more than smart robots but the robots
05:08 are beginning to learn
05:16 artificial intelligence also plays an
05:19 important role in a story that began in
05:21 January 1982 in Mount Washington New
05:24 Hampshire Hugh hare was 17 years old at
05:27 the time together with his friend Jeff
05:30 but Sir Hugh went up a mountain but they
05:33 were caught off guard by a change in the
05:35 weather a blizzard raged for three whole
05:38 days the missing boys were only found
05:41 after four days both were alive but they
05:44 had severe frostbite
05:47 the doctors decided to amputate Hugh’s
05:49 legs just below the knee 32 years later
05:55 Hugh hare has AI legs which he developed
05:59 himself he spoke on turning disability
06:02 into opportunity at the TED conference
06:04 in 2014
06:05 [Music]
06:11 dancer Adrianne haslet-davis lost a leg
06:14 in 2013 in the terrorist attack at the
06:17 Boston Marathon thanks to the smart
06:19 prosthesis by Hugh hare she can dance
06:22 again
06:28 [Music]
06:41 [Music]
06:42 [Applause]
06:54 Boston home dear the Massachusetts
06:57 Institute of Technology
07:01 we met with Hugh here to talk about
07:03 artificial intelligence and the human
07:05 body he’s the pioneer in the field of
07:08 intelligent prosthetics a single person
07:10 who was both developer and user there
07:17 are dozens of prototypes in his lab so
07:20 this is you have a motor
07:22 you have here’s a motor and this is a
07:25 synthetic subtalar joint for inversion
07:27 e-version uh-huh so we’ve iterated and
07:32 spent millions of colors to arrive at
07:36 this optimal architecture Hugh began
07:39 developing prosthesis after his lower
07:41 limbs had been amputated his replacement
07:43 legs became increasingly complex now
07:46 they are AI limbs with countless sensus
07:49 motors and computers I quickly realized
07:54 that I had an opportunity that from my
07:56 knees down I was there was a blank slate
07:58 and I could create anything in that
08:01 space that I could conceive and imagine
08:05 and so I started as a young man I
08:07 started imagine what what that blank
08:11 space may look like what may fill that
08:13 space disability depends on perspective
08:19 hue hair has developed a novel answer
08:23 with the special prosthesis that he’s
08:25 developed himself he can once again
08:27 pursue his greatest passion climbing
08:43 [Music]
08:45 so there is a computer in here there’s
08:48 three actually three yeah and they’re
08:50 each the size of your thumbnail uh-huh
08:52 so very small micro processors and
08:55 there’s a muscle tendon like the motor
08:57 system mm-hmm
08:59 so the computer runs algorithms and
09:03 receives sensory information so the
09:06 device is measuring its position speed
09:07 accelerations temperatures and whatnot
09:10 all that information goes into the
09:11 computer that computer runs its
09:13 algorithms and then decides on the
09:16 actions of the muscle tendon like motor
09:18 system and this all happens very fast so
09:21 as I’m walking and going up and down
09:23 hills and steps it’s constantly
09:25 responding into my biomechanical needs
09:27 it is so good that even nowadays you do
09:31 my mountain climbing or you you still go
09:33 climbing absolutely and I run and you
09:36 cannot you cannot with a straight face
09:38 say that I’m disabled I trail run I play
09:44 tennis I’m mountain climb I do whatever
09:46 I want to do physically now if you
09:48 remove the technology from my body
09:50 mm-hm I’m severely disabled I’m crippled
09:53 but with the technology in this
09:55 sophisticated human machine interaction
09:57 I’m freed from the shackles of
09:59 disability
10:01 our intelligent prosthesis only the
10:04 beginning will technology increasingly
10:07 merge with the human body
10:13 intelligent humanoids have already been
10:15 depicted in feature films such as ex
10:17 machina you shouldn’t trust Nathan you
10:20 shouldn’t trust anything he says we’re
10:24 closing the loop between the synthetic
10:26 robotic limb and the human brain the
10:29 human nervous system
10:30 what that means is the person can think
10:34 send descending commands down through
10:37 the nerves and when we we measure those
10:39 commands and they control synthetic
10:42 motors on the Bionic limb and then we’re
10:45 also closing the loop so sensors in the
10:47 Bionic limb input information into the
10:50 nervous system so the person can feel
10:51 the Bionic limb moving its position its
10:55 sensations as if it’s part of their body
10:58 this is almost philosophical because you
11:00 have the body and you have the machine
11:04 and you sort of start merging them
11:07 together huh yeah and regaining evidence
11:10 that when a human being can feel a
11:12 synthetic body part when they when they
11:16 can touch it and it feels like normal
11:18 touch when they move it and it feels
11:20 like a normal joint movement that that
11:22 synthetic object becomes part of their
11:25 their body their identity their self
11:27 well what’s cool about having a
11:29 significant part of your body that’s
11:31 design and synthetic is you can upgrade
11:34 so given that I’m in a mighty professor
11:37 I’m upgraded every week I can do
11:40 software and hardware that’s interest so
11:42 I go I grow older right right and you
11:46 can get new synthetic part of my body is
11:49 improving in time my biological body is
11:51 degenerating which is very peculiar
11:56 for hue hair artificial intelligence is
12:00 a blessing by the time our interview was
12:03 over a snowstorm was raging in Boston an
12:06 interesting coincidence as this was also
12:09 how hues transition began thanks to AI
12:12 body and machine are slowly merging
12:17 [Music]
12:23 artificial intelligence is also
12:25 increasingly determining our
12:27 communication it’s there behind every
12:30 search hidden from view in social
12:33 networks intelligent algorithms control
12:35 what we see and thus influence what we
12:38 read and what we don’t but there’s a
12:41 problem
12:41 fate news capturing and reselling our
12:45 attention and our digital data has
12:47 become big business information
12:50 technology firms are among the most
12:52 valuable companies in the world Facebook
13:00 YouTube and Twitter have changed the
13:02 media worldwide but exactly what role do
13:05 their intelligent algorithms play in the
13:07 spread of fake news in 2018 a team of
13:14 scientists from Boston analyzed the
13:16 spread of fake news the study was led by
13:19 Professor scene and aural it was the
13:23 largest worldwide study that had ever
13:25 been conducted on the spread of fake
13:27 news on social networks while
13:32 information is abundant attention is
13:34 scarce so there’s way more information
13:38 than we can process and so these
13:40 platforms help us by curating the
13:43 information and as you said prioritizing
13:46 what comes first in our newsfeed what
13:49 comes second what comes third and they
13:51 have a machine an algorithm based on
13:54 machine learning that is deciding what
13:57 gets shown first second third or in fact
14:00 what gets shown at all some things are
14:02 not shown it’s not the case that every
14:04 piece of information is shown to
14:06 everyone but which criteria do Facebook
14:08 and Twitter use to program their
14:10 algorithms the incentives of the people
14:12 writing those algorithms are based on
14:14 the incentives of the platforms the
14:16 companies that they work for those
14:18 companies are based on an economic model
14:21 of engagement the more people are
14:24 engaged the more opportunities you have
14:26 to show ads and so you have more
14:30 inventory for advertisements but the
14:33 second important reason
14:35 is that the more people are engaged the
14:37 more you learn about who they are and
14:39 what they like and the more
14:40 sophisticated the targeting is in terms
14:44 of advertising so engagement is a key
14:46 factor for the for the economic success
14:49 of the social media industrial complex
14:53 daily internet usage is increasing
14:55 worldwide in 2018 in Germany the average
14:59 was over 3 hours a day for younger
15:01 people it was just under 6 hours a day
15:03 things that are exciting novel
15:07 surprising things that are potentially
15:10 shocking are more likely to be engaging
15:15 clicked on read viewed shared liked and
15:20 therefore there are elements of the
15:24 models that determine the newsfeed that
15:27 favor engagement the following case from
15:30 Japan shows what fake news and social
15:32 networks can lead to
15:38 video showing young women who allegedly
15:41 became ill after a cervical cancer
15:42 vaccine were posted online at the same
15:46 time unverified scientific studies were
15:49 circulated on social networks both
15:52 videos and studies were picked up by
15:53 television this led to the vaccination
15:56 rate against cervical cancer in Japan
15:58 falling from 70 percent to less than 1%
16:02 how could it be that false information
16:04 could turn an industrialized country
16:07 like Japan against a globally recognized
16:09 vaccination
16:16 Hamburg where we meet rico marrero naka
16:19 the doctor had tried to counteract the
16:22 anti-vaccine hysteria and inform the
16:24 public online but then she was targeted
16:30 I was harshly attacked in the Twitter’s
16:33 or social media when I started writing
16:36 about it’s a safety of the vaccines they
16:39 even tried to threaten me by sending all
16:44 those blackmailing messages to my family
16:48 or me
16:54 Rikka continued undeterred she analyzed
16:57 the vaccination opponents facts checked
17:00 the scientific validity of their
17:02 experiments and published her results in
17:04 a book after all I was just stormed and
17:08 you know by the the criticism and one
17:11 day I just decided to shut up Twitter
17:13 for one for a while but
17:17 well I got them but your mother
17:19 surprised actually Joel mother surprise
17:22 became a Twitter trend of Japan but even
17:25 that didn’t change public opinion in
17:27 Japan despite top scientists sharing
17:30 because view she eventually lost the
17:32 battle to fake news they accused me
17:35 because my writing is wrong and my
17:38 writing is giving wrong impact to the
17:41 society and I’m hiding the truth but
17:44 it’s not it’s the contrary I’m telling
17:46 the truth
17:47 and people feel I’m hiding the truth
17:49 it’s really interesting isn’t it the
17:53 w-h-o sees the anti-vaccine movement as
17:55 a global health threat in Japan around
17:57 3,000 women will probably die every year
18:00 from cervical cancer because they choose
18:02 not to get vaccinated fake news can be
18:05 fatal
18:05 the false information is moving through
18:09 human society in a digital sense like
18:13 lightning while the truth is essentially
18:15 you know at the speed of molasses sort
18:18 of dripping very slowly from person to
18:21 person to person
18:23 the spread of false information shown
18:25 here in orange and correct information
18:28 see here in blue seen an aryl has
18:32 studied these patterns on Twitter more
18:33 closely than anyone else false news
18:39 travelled further faster deeper and more
18:42 broadly than the truth in every category
18:44 of information that we studied sometimes
18:46 by an order of magnitude difference and
18:49 this was particularly true of false
18:52 political news which was the most viral
18:55 category of any type of false news that
18:58 we studied fake news we are fighting the
19:01 fake news as you say fake news that news
19:10 has changed the political climate
19:12 worldwide social networks and their
19:20 intelligent algorithms are increasing
19:22 division in society they vie for our
19:25 attention feeding us exactly the
19:28 information we like what cans a click
19:31 rates quotas and length of stay and not
19:34 where the content is true or trustworthy
19:37 this personalized communication is
19:40 dividing our society social networks
19:42 assign each user a profile depending on
19:45 what she or he clicks on reads or
19:47 watches those belonging to the red group
19:50 here are mainly supplied with
19:51 information that matches the red profile
19:54 thus our filter bubble is gradually
19:56 formed
20:03 [Music]
20:05 everyone lives within their own network
20:08 our opinion is echoed by like-minded
20:11 people contradictory information and
20:13 opinions hardly enter our bubbles
20:16 [Music]
20:22 Media should be a mirror of society but
20:25 the AI algorithms are distorting the
20:27 opinions we form based off our media
20:30 consumption yet the media is too
20:32 important to be left to people who are
20:34 just out to make money how will our
20:41 visual intelligence change conflicts
20:44 what about intelligent autonomous
20:46 weapons the military is already testing
20:50 prototypes like here in California two
20:53 fighter jets launch a swarm of
20:54 intelligent drones the autonomous flying
20:57 objects then identify their own targets
21:00 should machines be allowed to take
21:02 life-or-death decisions
21:11 we traveled to meet one of the most
21:12 respected ethicists on autonomous
21:14 weapons in the US he warns of
21:20 uncontrollable development and is
21:22 committed to a worldwide ban on
21:23 autonomous weapons we visited Yale
21:27 professor Wendell gelding in his house
21:28 north of New York
21:33 sometimes people do not fully understand
21:36 what lethal autonomous weapons systems
21:38 are they tend to think of drones that
21:40 might have facial recognition software
21:42 and would pick off a terrorist that it
21:46 sees in the distance or perhaps a few
21:49 robotic soldiers on a battlefield
21:52 what is sometimes not fully appreciated
21:55 is lethal autonomy is not a weapon
21:59 system it is feature sets which can be
22:03 added to any weapon system and that
22:05 includes atomic weapons or other
22:08 high-powered munitions and the feature
22:11 sets would be the ability to pick a
22:13 target and destroy that target with
22:16 little or no active human intervention
22:20 intelligence image recognition automatic
22:24 target recognition these AI techniques
22:27 are already available the global
22:30 armament race has begun but machines do
22:33 not make life-and-death decisions about
22:35 humans humans make life-and-death
22:37 decisions about humans and when we
22:40 opened the door to machines making those
22:43 decisions we undermine the basic
22:45 principle of a responsible human agent
22:49 lethal autonomous weapons and
22:51 self-driving cars they are just the tip
22:54 of an iceberg with something much larger
22:59 below the surface and that larger thing
23:02 below the surface is autonomy in general
23:05 is autonomous systems in general
23:07 autonomous systems threaten to undermine
23:11 the foundational principle that there’s
23:13 an agent and that agent can either be a
23:15 human or it can be a corporation or
23:17 something else but that there is an
23:20 agent who is responsible and potentially
23:23 culpable and liable for its actions or
23:28 for any actions that are taken I don’t I
23:32 can’t think of anything more stupid than
23:35 humanity going down a route where we
23:37 have diluted the principle of
23:39 responsibility where we dilute it in
23:41 such a way that nobody can be held
23:44 responsible
23:45 anymore if something truly dire takes
23:48 place in the past we have been too slow
23:54 to recognize we were going down a wrong
23:56 path
23:57 we need a worldwide ban on autonomous
24:00 intelligent weapons
24:01 [Music]
24:05 artificial intelligence will
24:07 revolutionize industry in Germany the
24:10 term industry 4.0 has become a buzzword
24:13 cars robotic tools and entire production
24:16 plants are being linked via sensors and
24:19 equipped with AI but how well German
24:22 companies fare in worldwide competition
24:25 dr. Michele Bala is the head of the
24:28 Bosch Research Centre in running in
24:30 baden-wuerttemberg
24:33 artificial intelligence is one of the
24:35 main focus points here then the
24:43 intimidating when it comes to industrial
24:46 AI the AI that plays a role in products
24:49 then I think that the technology
24:51 companies that have decades of
24:52 experience in the physical world in real
24:54 life objects and the corresponding
24:56 experience in development and production
24:58 have a competitive advantage when adding
25:01 in machine learning and artificial
25:03 intelligence they have an advantage of
25:06 her companies that come purely from the
25:08 virtual worlds so I’m confident and this
25:13 is also the reason why we’re investing
25:15 so much in this area and why we’re
25:18 rolling out and applying this expertise
25:20 across the group
25:22 these are competence in concern also
25:25 anything
25:28 Germany has faith in its decades-long
25:30 technical expertise together with AI one
25:38 player who was fighting to get ahead is
25:39 China changing of the guard at the gate
25:42 of heavenly peace and Beijing soldiers
25:46 Flags Mao this was the old image of the
25:50 country but modern-day China has
25:53 awakened digitalization and artificial
25:58 intelligence promised a brave new world
26:00 an entire nation seems intoxicated by
26:04 its own progress where does this
26:12 palpable euphoria about the future stem
26:14 from
26:16 we meet home yang she’s Chinese and has
26:20 worked for a German company for several
26:22 years we asked her what is different in
26:24 China casually speaking we are different
26:27 no in the in the treasurer’s in our
26:32 thinking we are more open to the you
26:34 know the latest technology and opened
26:37 for the world yeah so probably you can
26:42 see from how much we are using the
26:45 smartphone right like like just now we
26:50 buy the coffee with a smartphone and we
26:53 pay for the taxi Bo with a smartphone
26:55 and sometimes my German colleagues they
26:58 are astonished to say that you don’t
27:00 even to have to bring cash with you and
27:03 I said yeah that’s normal life in Dutch
27:06 I always forget my wallet when I’m in
27:08 Germany because here in China I pay for
27:10 everything with my smart phone if you go
27:11 to the market and there’s an 80 year old
27:13 woman selling produce you might think I
27:14 guess I’ll pay with cash but you can
27:16 anymore you’ll be buying an apple and
27:18 she’ll take out a QR code scan it and
27:20 then you pay for it with your smartphone
27:21 it’s unbelievable there’s nothing like
27:23 that in Germany it’s crazy there’s
27:26 conserfund or China because it is the
27:28 London for example if I have one dinner
27:31 with my friends and we offer has true
27:35 you know hand hand out your phones and
27:37 we put sauce on the table uh-huh and
27:40 then if somebody’s picking up the phone
27:43 my course or by text message or by
27:47 WeChat message he has to pay for the
27:49 video that’s the punishment so we can
27:52 feel the advantage of the technology but
27:55 when you get used to it you start to
27:57 reflect what kind of impact to my life
28:00 what is the good part of us at a bad
28:02 part and then in terms of bad part I
28:05 mean by nature everyone will start to
28:08 think about how can I get rid of the bad
28:10 part but still trying to keep the good
28:12 part
28:13 young China is catching up and the whole
28:16 nation is hungry for progress
28:20 my sister’s Poisson Sheena dish with
28:23 speed just think about where China was
28:26 40 years ago and now things are going
28:28 full throttle when Jets take a dollar so
28:31 he touched upon gobble
28:33 China has even surpassed the u.s. when
28:35 it comes to filing AI patents one
28:38 example is the mobile transportation
28:40 service DD active and 400 Chinese cities
28:43 the platform organizes some 30 million
28:45 trips a day each DD vehicle is equipped
28:51 with a data logger which registers
28:53 whether the car is stationary or moving
28:54 or whether there’s a traffic jam the
28:57 data is also used to improve information
28:59 on traffic flow in big cities we call
29:02 this real-time traffic information TTI
29:04 or our TTI the DB data go directly into
29:08 the our TTI which gives you a much more
29:10 reliable view of whether roads are
29:12 congested or not than in German cities
29:14 connecting everything with everything
29:16 else and generating added value from
29:18 that the Chinese are really really good
29:20 at that condition isn’t sure his dish
29:23 dish cool
29:24 [Music]
29:33 China is a much younger nation there’s a
29:36 lot of energy and enthusiasm about
29:38 what’s to come there are a lot of
29:40 investments there are great education
29:42 institutions here for example Ching hua
29:44 University in Beijing and Tongji
29:45 University in Shanghai a really top
29:47 level there are really many capable
29:49 people the talent pool here is huge so I
29:52 think there’s a good chance that China
29:54 will be leading you know Dawkins format
29:56 once it’s predicted that in 2030 37
30:00 percent of all scientists will be
30:01 Chinese only 1.4 percent will be from
30:04 Germany and while Germany is currently
30:09 facing a lack of science teachers at
30:11 schools a young generation of innovators
30:13 is growing up in China they have top
30:16 level education fresh ideas and they’re
30:19 hungry for success one example is the
30:23 DJI company from Shenzhen it was founded
30:26 in 2006 by a young engineer today it’s
30:30 the world’s largest manufacturer of
30:32 civilian drones chief development
30:36 officer Martin Vandenberg shows us the
30:38 latest model
30:42 the new drone users intelligent image
30:44 recognition and can independently pursue
30:47 its target
30:55 equipped with dozens of senses and smart
30:58 positioning it can detect obstacles such
31:01 as trees or bushes
31:06 the tree saves my life no fool the tree
31:12 was the limit because the drone says no
31:16 in this case the drone said I can’t fly
31:19 through it safely so I better stop as I
31:21 understand it it’s very common here in
31:23 China to combine things
31:24 facial-recognition navigating flying yes
31:30 and in this case only visuals were used
31:32 you don’t have a Bluetooth transmitter
31:34 or anything else on you the pilot simply
31:36 says I want to follow Rangga on the
31:38 display confirms and the drone follows
31:40 you innovation made in China Chinese
31:47 television proudly reports such
31:48 successes China no longer copies modern
31:53 day China invents DJI is truly the first
31:58 global brand with a completely new
32:00 product range from China before China
32:03 was primarily known as the world’s
32:04 factory that’s changing now and the
32:06 perception is also changing our company
32:09 alone employs almost 3000 engineers
32:12 really smart people they’re all
32:14 enthusiastic they’re motivated they want
32:16 to create something new and that’s the
32:19 spirit that prevails in this country
32:20 that’s going to persist a hunger for
32:23 innovation is what defines modern China
32:25 and estate for this modernity an China’s
32:30 economy is booming cities such as Qin
32:33 Jen Chengdu or Gwangju have the same
32:35 economic output as entire European
32:38 countries
32:43 the country is investing in its young
32:46 people take the example of Robo master
32:49 here a team is preparing for the
32:51 upcoming season each team has to program
32:54 and optimize a gaming robot the final is
32:57 a nationwide event
33:01 Troy Quinn supervises the competition
33:04 and shows us the parkour that the new
33:10 the latest ones open also we have a it
33:14 doesn’t shoot right now as we can see
33:19 there is a petal right here and it’s the
33:22 reference systems is for sensing the
33:25 bullets while you strike it you can see
33:28 it fashions yeah that means you hit a
33:30 fitting and yeah and decrease the hell
33:34 if the one robots lose all the points it
33:37 will give a shutdown this may look like
33:42 a game but it’s actually a program to
33:44 support young engineers it was launched
33:46 by DJI and now several other companies
33:49 are also involved the next generation of
33:52 Engineers needs to be good at designing
33:54 and programming and that’s exactly what
33:57 they’re learning here in a playful
33:58 context the background is serious
34:04 engineering or more game is serious and
34:07 Union because you you need to build a
34:10 new robot you not only put them together
34:13 it’s just the first step and then you
34:16 need to the code and do some coding do
34:18 some artificial recognitions there’s a
34:20 toe is quite a massive work and it’s not
34:25 that simple for maybe for college
34:26 students how many universities are in
34:29 their study to a university in the bio
34:32 to women’s but for this year we have 170
34:35 in Tangier and 74 all over the world
34:38 registers for the accommodations and how
34:41 are the trainees
34:42 about 140 and how good are they mmm we
34:47 can resize it okay maybe we can get
34:50 hands on the robots and your sphere is
34:52 how it works okay we have two robots so
34:54 we will now try and okay have a look at
34:57 it okay Troy needs just a few key
35:07 combinations to control the robot go
35:10 forwards backwards or forwards I’m an
35:15 old guy you know oops
35:18 you can’t see me and now now you fire at
35:23 me no I fired you I can fire me but we
35:26 are teammates why the final is a major
35:37 event 20,000 people are in the audience
35:40 another 30 million watch the contest
35:43 online engineers and programmers are
35:46 China’s new pop stars
35:51 they really care about this game because
35:54 the teams from their schools win the
35:57 glory from the games that means a lot
35:59 sport for them the students invested
36:04 thousands of hours and to developing
36:06 their robots the winner of the 2018
36:08 competition was the team from southern
36:10 China University of Technology all of
36:13 China
36:14 celebrated their success
36:16 [Music]
36:21 there are also excellent initiatives in
36:23 Germany one example is the so-called adn
36:26 Expo in Hannover around 300,000 students
36:30 come here within a single week it’s the
36:32 largest classroom in Europe young people
36:36 are introduced to new tech learn had a
36:38 program and design new circuits and they
36:41 seem to love it but in contrast to China
36:46 German media hardly take note Germany
36:50 talks too little about its successors we
36:53 too can keep up with the global AI race
36:55 if we want to but we really have to step
36:58 on the gas
37:08 children are now growing up with
37:10 smartphones internet an intelligent toys
37:13 but what does that do to children we met
37:17 scientist Stefania druga to discuss
37:19 following research in Boston she’s
37:22 currently working in Berlin
37:28 a generation after the internet
37:30 generation is the AI generation they’re
37:33 growing up surrounded by AI like Alexa
37:37 for example
37:39 [Music]
37:41 you know this device was not designed
37:44 for children right this device was
37:46 designed for households so families make
37:49 purchases via this device and I think
37:51 it’s very important to recognize when we
37:53 talk about kids and when we talk about
37:55 regulation for smart toys and regulation
37:59 of devices that record data about our
38:02 kids to understand whom a made these
38:05 devices and what was the ultimate goal
38:07 of this device just imagine you in some
38:10 years you might also have children would
38:12 you sort of install Aleksei in your home
38:15 it depends what Alexa platform would
38:17 look like then where I would live right
38:21 now no Alexa how many seconds are there
38:25 in a year calendar year has 31 million
38:30 536,000 seconds and a leap year has 31
38:34 million six hundred and twenty two
38:35 thousand four hundred seconds that’s not
38:39 what I wanted to know unlike Alexa the
38:43 small robot Cosmo was developed for
38:45 children trust and intelligence are
38:50 related because if I think that a device
38:52 is smart I tend to trust it more so the
38:56 younger children weren’t so sure how
38:58 smart these devices were and all their
39:01 children they thought they’re smart
39:03 because they have a lot of data so
39:05 basically the children were like three
39:08 and a half four to six we’re more
39:10 skeptical initially of these devices and
39:12 as soon as kids would go to school they
39:15 were more like trustworthy just because
39:17 they saw how much information these
39:19 devices have curiosity creativity
39:23 imagination children are open-minded and
39:27 enjoy trying things out some devices
39:30 react as if they were artificial living
39:32 beings
39:37 after they learn how to program it and
39:39 train it both the young children and the
39:42 old children became more skeptical and
39:44 trusted the device less so they
39:47 understood it knows how to answer this
39:49 type of questions but it doesn’t know
39:50 how to answer this type of questions do
39:55 you trust machines are they smart what
39:59 can you do that they can’t stefania
40:03 calls for increased understanding of
40:05 artificial intelligence the goal of my
40:08 research is to bring this AI literacy
40:11 both to kids and parents because these
40:14 are in the home and parents are they’re
40:16 also part of the conversation asking
40:18 questions and I think it’s important for
40:22 families to understand how AI works in
40:25 order to make a good use of this
40:27 technology some things promote our own
40:31 creativity other things seem to be
40:33 superfluous and there’s another
40:37 important difference between humans and
40:40 machines a friend can sleep over at your
40:45 house and tell you stories she talks to
40:48 you if you play with a robot it can only
40:52 do certain things Cosmo can only play
40:55 with dice Alexa can only answer
40:58 questions or sing a song and that ball
41:00 can only roll Julia can do all those
41:05 things you just have to get a lot of
41:10 single things from one robot or get
41:12 other robots first this one then that
41:14 other than the next and with a person
41:17 you don’t have to get a new one you
41:18 always have them there and they can do
41:20 everything
41:25 Marie and Yulia hit the nail on the head
41:28 the robot cannot replace a best friend
41:31 no matter how smart it is a machine
41:34 cannot substitute a human being
41:39 here our journey through the world of
41:41 artificial intelligence draws to a close
41:43 there will be major changes but it is
41:47 not the machines but we humans who
41:49 caused them we not only have the freedom
41:52 but also the responsibility to shape our
41:55 own future
41:56 [Music]
42:16 you
42:19 [Music]

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