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This week, we are looking at the
future of work. But which jobs will | 0:00:01 | 0:00:06 | |
go to the robots? Stock pickers?
Nurses? My? | 0:00:06 | 0:00:17 | |
Mr Victor Sherlock of Horsham has a
robot at the bottom of his garden | 0:00:39 | 0:00:42 | |
and he wants to teach it to mow the
lawn. Artificial intelligence. | 0:00:42 | 0:00:46 | |
Everyone is talking about it. Over
the years we have seen a developer. | 0:00:46 | 0:00:51 | |
We have seen it evolve. This is the
Watson that won Jeopardy. We have | 0:00:51 | 0:00:59 | |
travelled the world to see AI ever
tries to treat cancer. It studies of | 0:00:59 | 0:01:02 | |
millions, tens of millions of
examples from the assemblage of | 0:01:02 | 0:01:05 | |
scientific literature. Predict
crime. Understand the economy. | 0:01:05 | 0:01:14 | |
Chamois companies with revenue
between $25 million and $60 million. | 0:01:14 | 0:01:19 | |
-- show me company 's. And save the
world. So it may not yet have | 0:01:19 | 0:01:25 | |
conquered DIY. But we keep hearing
how a I will change everything. | 0:01:25 | 0:01:36 | |
Technology will make man be more
intelligent form of expression. | 0:01:36 | 0:01:44 | |
However, it is the bad side of these
algorithms that always seems to get | 0:01:44 | 0:01:48 | |
the headlines. The fear that
tireless robots infused with | 0:01:48 | 0:01:52 | |
artificially intelligent brains may
one day do us all out of our jobs. | 0:01:52 | 0:01:57 | |
With interaction, with use, with
feedback is actually gets gradually | 0:01:57 | 0:02:02 | |
smarter. From bots that can talk
like Arthur. You want to help you | 0:02:02 | 0:02:07 | |
reset your password question mark
walk like a. And even, perhaps, | 0:02:07 | 0:02:10 | |
think like us. We have been warned
that the fourth Industrial | 0:02:10 | 0:02:19 | |
Revolution is coming. The biggest
difference between us and the | 0:02:19 | 0:02:24 | |
Industrial Revolution in the 1800s
if the speed. Millions of workers | 0:02:24 | 0:02:28 | |
are on the move. So what is going
on? What jobs are really at risk? | 0:02:28 | 0:02:41 | |
What is the future of work? And we
start with healthcare workers. For | 0:02:41 | 0:02:49 | |
almost 70 years, the UK's a national
health service has been a free | 0:02:49 | 0:02:54 | |
service at the point of care. That
model is under strain as the | 0:02:54 | 0:02:57 | |
population ages and chronic health
and conditions increase while | 0:02:57 | 0:03:02 | |
resources shrink. A recent study
showed that almost two thirds of | 0:03:02 | 0:03:07 | |
doctors think that patient safety
has deteriorated with one dog to say | 0:03:07 | 0:03:10 | |
in we are not robots. We are human
staff needs. Should be NHS turned to | 0:03:10 | 0:03:17 | |
robots to ease the strain on human
staff? Jane has been looking at how | 0:03:17 | 0:03:22 | |
data driven technology could
transform care in the NHS. Good | 0:03:22 | 0:03:28 | |
artificial intelligence help save
the NHS? More people are looking at | 0:03:28 | 0:03:33 | |
innovative ways to ease the workload
of doctors and nurses. Computer | 0:03:33 | 0:03:38 | |
programmes can rapidly analyse huge
quantities of information in ways | 0:03:38 | 0:03:43 | |
that humans do not have the time nor
brain capacity to do. In 2016, | 0:03:43 | 0:03:52 | |
Google's the mind was developing an
algorithm to identify abnormalities | 0:03:52 | 0:03:55 | |
in eye scans. Now it has submitted
its findings to a medical journal | 0:03:55 | 0:03:59 | |
for review. It could mean its
systems are more effective than | 0:03:59 | 0:04:04 | |
humans at diagnosing eye disease.
Deep mined taught its machine | 0:04:04 | 0:04:09 | |
learning software using a million
eye scans. I will see three other | 0:04:09 | 0:04:12 | |
projects integrating AI and data
gathering. Dementia is now the | 0:04:12 | 0:04:21 | |
leading cause of death in the UK. At
this hospital in carpentry, software | 0:04:21 | 0:04:26 | |
is being tested to remotely monitor
patients. This is one of the rooms | 0:04:26 | 0:04:33 | |
on the ward. It looks at any other
hospital room except in this one | 0:04:33 | 0:04:37 | |
there are two infrared illuminators
and a sensor monitoring my movements | 0:04:37 | 0:04:43 | |
including when I'm asleep. Oxy
health uses a standard digital | 0:04:43 | 0:04:49 | |
camera and a tongue twisting
science. Everytime your heartbeat is | 0:04:49 | 0:04:53 | |
your skin briefly flashes red. We
can not see this but the sensor in | 0:04:53 | 0:05:00 | |
the camera can detect these
so-called micro- blushers. It even | 0:05:00 | 0:05:03 | |
picks up my vital signs when I am
hiding under a table in the room as | 0:05:03 | 0:05:07 | |
those micro- blushers can still be
seen on my arm. There is an alert if | 0:05:07 | 0:05:11 | |
I leave my bed. And the nurse can
click on a live feed to see what is | 0:05:11 | 0:05:18 | |
happening and determine whether they
need to come and check on me right | 0:05:18 | 0:05:21 | |
away. For the staff, initially, when
it looked like we had a camera in a | 0:05:21 | 0:05:25 | |
box in a room they were not happy
about it. But when we spent some | 0:05:25 | 0:05:30 | |
time with Oxy house, they explained
to them and they see how it works, | 0:05:30 | 0:05:34 | |
they love it. They love the fact it
gives you an extra... And extra | 0:05:34 | 0:05:38 | |
support. The project is in the pilot
stage in is awaiting medical | 0:05:38 | 0:05:45 | |
certification. The data collected is
being analysed remotely by a team in | 0:05:45 | 0:05:49 | |
Oxford and will be used to train the
programme to be more predictive. We | 0:05:49 | 0:05:53 | |
have never had these capabilities as
a species, to constantly get our | 0:05:53 | 0:05:57 | |
rate reading and routine data. There
is no reason as we combine and with | 0:05:57 | 0:06:02 | |
use the data using a I we cannot
detect the onset of dementia or read | 0:06:02 | 0:06:06 | |
getting worse. We can detect
problems early so you can stay in | 0:06:06 | 0:06:09 | |
your own home or a comfortable
setting without coming into | 0:06:09 | 0:06:12 | |
hospital. That will save a huge
amount of time. Saving critical time | 0:06:12 | 0:06:18 | |
was the motivation behind automating
processes at NHS blood and | 0:06:18 | 0:06:21 | |
transplant. 4500 people receive a
transplant each year but 6500 are on | 0:06:21 | 0:06:28 | |
the list. Everyday, three people die
waiting for a transplant. A lot of | 0:06:28 | 0:06:34 | |
information needs to be sifted
through to make life-and-death | 0:06:34 | 0:06:36 | |
decisions. The NHS is now using
public cloud technology from IBM to | 0:06:36 | 0:06:42 | |
help maintain huge databases that
used to be managed with a marker and | 0:06:42 | 0:06:46 | |
a whiteboard. By working with some
of this automated technology we can | 0:06:46 | 0:06:50 | |
make sure we are making the best
possible decisions and that our | 0:06:50 | 0:06:54 | |
clinical teams are thinking through
the best outcomes for all of the | 0:06:54 | 0:06:57 | |
patients on the waiting list, and
that our staff, who are often | 0:06:57 | 0:07:02 | |
working until three in the morning
in a high-pressure environment, | 0:07:02 | 0:07:05 | |
needing to allocate organs quickly,
they are supported by this | 0:07:05 | 0:07:08 | |
technology. Collecting all this
personal data has led some to ask if | 0:07:08 | 0:07:13 | |
it is stored securely enough. At the
challenges have in the public sector | 0:07:13 | 0:07:16 | |
is the perception that maybe the
public is less secure than an on | 0:07:16 | 0:07:22 | |
premises data centre which is not
the case. We have an obligation, | 0:07:22 | 0:07:26 | |
obviously, to many customers to
ensure that the public cloud is kept | 0:07:26 | 0:07:30 | |
secure and patched and maintained
effectively. The fallout from it not | 0:07:30 | 0:07:34 | |
being in that condition is quite
severe. In the future, the team | 0:07:34 | 0:07:39 | |
hopes that artificial intelligence
will be able to predict how long | 0:07:39 | 0:07:43 | |
people will be on the waiting list
for an organ. There is an average | 0:07:43 | 0:07:47 | |
waiting time of two weeks to see a
doctor in the UK. Disk and drop to | 0:07:47 | 0:07:51 | |
two hours if you register with AGP
at hand. You can sign up if you live | 0:07:51 | 0:07:57 | |
or work within certain zones of
London. You need to give up your | 0:07:57 | 0:08:01 | |
regular prat doctor and register
with the remote surgery. 26,000 | 0:08:01 | 0:08:04 | |
people have registered so far. I had
a chance to test it out, pretending | 0:08:04 | 0:08:10 | |
I had a case of food poisoning.
First I went through a triage with a | 0:08:10 | 0:08:16 | |
chat bot on an app who recommended I
speak remotely to a real-life | 0:08:16 | 0:08:19 | |
Doctor. The doctor recommends
further care and can even send a | 0:08:19 | 0:08:25 | |
prescription to a pharmacy. The
artificial intelligence in the app | 0:08:25 | 0:08:29 | |
draws on union of data points and
can cross referenced the latest | 0:08:29 | 0:08:33 | |
medical research from journals and
studies around the world. You use | 0:08:33 | 0:08:38 | |
artificial intelligence to tell you
whether or not to see a doctor. You | 0:08:38 | 0:08:41 | |
are always free to see a doctor
anyway but what we find is that 40% | 0:08:41 | 0:08:46 | |
of the people who are reassured that
they have everything they need, | 0:08:46 | 0:08:53 | |
based at there. The app has faced
witticism from the Royal College of | 0:08:53 | 0:08:57 | |
GPs to say that younger users are
being cherry picked for the service. | 0:08:57 | 0:09:01 | |
NHS England lodged a formal
objection to the plan out -- rollout | 0:09:01 | 0:09:06 | |
beyond London. I have think we need
to give people safe and equitable | 0:09:06 | 0:09:14 | |
care. If we roll things out too
quickly without ensuring that safety | 0:09:14 | 0:09:18 | |
and fairness with Ryan the risk of
causing unintended harm. So it is | 0:09:18 | 0:09:22 | |
wise and sensible but independent
evaluation is now going on of these | 0:09:22 | 0:09:26 | |
new technologies so that people can
be reassured that they are safe and | 0:09:26 | 0:09:29 | |
they are fine for everybody. I think
it is wrong. I genuinely think that | 0:09:29 | 0:09:35 | |
it is just not right. I cannot
understand why people are hesitant. | 0:09:35 | 0:09:40 | |
Often it is because they are scared
of new technology. They do not know | 0:09:40 | 0:09:48 | |
what the consequences are. And that
is fine. They need to check that and | 0:09:48 | 0:09:51 | |
reassure them self. There is nothing
wrong with that. I have seen three | 0:09:51 | 0:09:55 | |
ways companies are working with data
to help with monitoring, automation | 0:09:55 | 0:09:58 | |
and decreasing waiting times. All
areas that could help an | 0:09:58 | 0:10:03 | |
overstressed health service. Could
artificial intelligence helped to | 0:10:03 | 0:10:06 | |
save the NHS? It is an exciting
development worldwide but never more | 0:10:06 | 0:10:13 | |
so then here and there are certainly
things AI can help as we to plough | 0:10:13 | 0:10:17 | |
through data we already have, and
the questions we didn't even know | 0:10:17 | 0:10:21 | |
needed answering. But let's be
clear, AI2 will never replace | 0:10:21 | 0:10:28 | |
person-to-person interaction. The
touch of a doctor, the looking deep | 0:10:28 | 0:10:31 | |
into someone's eyes and recognising
that the make-up of the person is | 0:10:31 | 0:10:37 | |
what matters, not just a bleeding
leg or a headache. It is much more | 0:10:37 | 0:10:41 | |
than that and it will be quite a
long time for a match creatures | 0:10:41 | 0:10:45 | |
that. You think it ever will? I will
be stunned if win -- within my | 0:10:45 | 0:10:53 | |
lifetime AI2 ever replaces Doctor. | 0:10:53 | 0:10:58 | |
That was Jen and although we are | 0:10:58 | 0:11:00 | |
seeing | 0:11:00 | 0:11:00 | |
That was Jen and although we are
seeing automation | 0:11:00 | 0:11:00 | |
That was Jen and although we are
seeing automation creep | 0:11:01 | 0:11:01 | |
That was Jen and although we are
seeing automation creep in to the | 0:11:01 | 0:11:02 | |
skilled workforce, | 0:11:02 | 0:11:02 | |
seeing automation creep in to the
skilled workforce, we often think of | 0:11:02 | 0:11:05 | |
it as working in the low skilled
sector where the jobs are | 0:11:05 | 0:11:08 | |
repetitive. But what about bases in
the world where they still have a | 0:11:08 | 0:11:12 | |
ready supply of relatively low
skilled at cheap human workers? You | 0:11:12 | 0:11:16 | |
would expect countries like China
for example to be able to hold back | 0:11:16 | 0:11:20 | |
the robot tied longer than most.
Will not so. We sent Danny Vincent | 0:11:20 | 0:11:25 | |
to a warehouse owned by the giant
Chinese online retailer Alibaba. | 0:11:25 | 0:11:33 | |
This is a 3000 square metre
warehouse. It is part of an | 0:11:33 | 0:11:37 | |
operation that sorts and delivers 85
million packages at a, shipped to | 0:11:37 | 0:11:43 | |
over 200 countries around the world.
Products are packed and sorted here. | 0:11:43 | 0:11:47 | |
Usually by dozens of workers. But
recently, they had some new | 0:11:47 | 0:11:52 | |
recruits. 148 automated guided
vehicles navigate the floors of this | 0:11:52 | 0:12:01 | |
warehouse. These agile bots can
communicate to each other to avoid | 0:12:01 | 0:12:06 | |
collisions and distribute the work
amongst themselves. A bit like their | 0:12:06 | 0:12:11 | |
human counterparts who still take on
the final stages of processing. Li | 0:12:11 | 0:12:19 | |
Yen is a 28-year-old worker from
south-western China. Her job now is | 0:12:19 | 0:12:24 | |
in part done by these machines. She
followed a family tradition of | 0:12:24 | 0:12:29 | |
migrating thousands of miles to find
better paid work. TRANSLATION: It | 0:12:29 | 0:12:34 | |
saves me from work, Kwok into every
shelf to pick up the goods. Now I | 0:12:34 | 0:12:42 | |
just had to stand at the pickup
platform to wait for the robots to | 0:12:42 | 0:12:46 | |
send me the goods and I don't have
to constantly walk here and there. | 0:12:46 | 0:12:51 | |
They are part of a data system
collecting information not just | 0:12:51 | 0:12:55 | |
about their environments are also
the sales patterns, understanding | 0:12:55 | 0:12:58 | |
what sells more regularly,
rearranging where products are | 0:12:58 | 0:13:01 | |
placed, shaving off valuable minutes
on overall delivery time. | 0:13:01 | 0:13:12 | |
TRANSLATION: In a traditional
warehouse, it is purely manual. | 0:13:12 | 0:13:14 | |
There are so many products, so the
job for the human workers is very | 0:13:14 | 0:13:18 | |
heavy. A could walk that they could
walk 50,000 steps per day. Here the | 0:13:18 | 0:13:24 | |
machines do all of that, making the
work easier and more efficient. | 0:13:24 | 0:13:28 | |
China has the largest workforce in
the world. But it is shrinking and | 0:13:28 | 0:13:32 | |
rising labour cost two is making it
harder for logistic companies to | 0:13:32 | 0:13:36 | |
recruit low skilled workers. China
is already leading the development | 0:13:36 | 0:13:41 | |
of dark factories, factories that
need no human workers and can | 0:13:41 | 0:13:45 | |
literally work with the lights off.
But will automation replace workers | 0:13:45 | 0:13:49 | |
like Li Yen? TRANSLATION: I feel
these robots would become my | 0:13:49 | 0:13:54 | |
competitors because of sorting out
goods I can do other work, monitor | 0:13:54 | 0:13:59 | |
the system, it takes orders and
other work. I don't think they will | 0:13:59 | 0:14:04 | |
affect me. Alibaba and its partners
say automation is an irreversible | 0:14:04 | 0:14:09 | |
trend in China but they see sectors
like e-commerce were born out of | 0:14:09 | 0:14:13 | |
innovation. Online shops replace
many high-street stores, but they | 0:14:13 | 0:14:16 | |
insist their workers are machines --
and machines will continue to work | 0:14:16 | 0:14:22 | |
together. | 0:14:22 | 0:14:23 | |
We are going to interrupt this
broadcast with some breaking news | 0:14:28 | 0:14:31 | |
coming into us at the BBC. It is a
world first, ABC click presenter | 0:14:31 | 0:14:36 | |
Spencer Kelly has been replaced by a
robot. It has been dubbed RoboSpen | 0:14:36 | 0:14:43 | |
and it is apparently capable of a
whole host of emotions as well as | 0:14:43 | 0:14:50 | |
understanding and writing stories
and crucially, he never forgets his | 0:14:50 | 0:14:53 | |
lines. RoboSpen joins the now from
the factory that created him. Over | 0:14:53 | 0:14:58 | |
to you. Sounds like you said I was
artificially intelligent. As a robot | 0:14:58 | 0:15:04 | |
I am often asked to post photos and
TV reports about AI. I am not | 0:15:04 | 0:15:09 | |
intelligent. Everything I am saying
is written by a human. The point is, | 0:15:09 | 0:15:16 | |
robots and AI are not the same
thing. Observe my articulated hands | 0:15:16 | 0:15:28 | |
powered by four fingers and bear
cylinders of. Engineering arts has | 0:15:28 | 0:15:32 | |
made a name for itself by making
robotic performance, actors and | 0:15:32 | 0:15:38 | |
communicators of. -- indicators.
Which, according to Will, is the | 0:15:38 | 0:15:45 | |
only reason the world might need
humanoid robot. AI great for | 0:15:45 | 0:15:53 | |
entertainment and communication, if
you want something that interacts | 0:15:53 | 0:15:56 | |
with people, the best way to do that
is to make something person shaped. | 0:15:56 | 0:16:00 | |
If you think Star Wars, the robot
that talks a lot, has a personality, | 0:16:00 | 0:16:07 | |
doesn't do a lot of useful thing.
Will and his team design and build | 0:16:07 | 0:16:13 | |
robots here from scratch from the
aluminium tones to the robbery | 0:16:13 | 0:16:17 | |
spines and plastic shells. -- bones.
The next wave our way into the | 0:16:17 | 0:16:26 | |
uncanny Valley. It has just come to
life with the eyes there. You have | 0:16:26 | 0:16:38 | |
seen silence of the lambs, haven't
you? That is very eerie, that is. | 0:16:38 | 0:16:46 | |
Will is fascinated with how the
human body works and a lot of this | 0:16:46 | 0:16:50 | |
research concentrates on making
natural looking body movements that | 0:16:50 | 0:16:53 | |
are also very quiet. It is something
that he believes might find a place | 0:16:53 | 0:16:58 | |
in the field of ethics, although he
says there is still a lot of work to | 0:16:58 | 0:17:02 | |
be done. I don't have a single
precision part in my body. How can I | 0:17:02 | 0:17:07 | |
achieve this level of precision with
these organic, bones and bits of | 0:17:07 | 0:17:12 | |
mushy flash. One of the biggest
problems we have is that there is | 0:17:12 | 0:17:16 | |
nothing as good as human muscle. It
so for all of this motor development | 0:17:16 | 0:17:19 | |
that we have done, we don't come
anywhere near to what a human can | 0:17:19 | 0:17:23 | |
do. Where you will see humanoid
robots, you will see them in a | 0:17:23 | 0:17:30 | |
commercial context, so you might get
into a shop and you might see a | 0:17:30 | 0:17:34 | |
robot in Derrida is trying to sell
you something. De worry about all | 0:17:34 | 0:17:37 | |
the clever AI, that's really going
to stay on your computer, on your | 0:17:37 | 0:17:41 | |
smart phone. -- don't worry. It
won't chase you up the stairs any | 0:17:41 | 0:17:47 | |
time soon. | 0:17:47 | 0:17:49 | |
The artificially intelligent
algorithms will is talking about | 0:17:54 | 0:17:56 | |
could very well change can assist
all replace some jobs. So what does | 0:17:56 | 0:18:01 | |
that mean for the field of
journalism? Now this is the BBC | 0:18:01 | 0:18:07 | |
newsroom and every minute of every
day there are a lot of conflicts | 0:18:07 | 0:18:11 | |
processes in place here. All of
these journalistss are taking in | 0:18:11 | 0:18:15 | |
huge amounts of information around
the world trying to work out what is | 0:18:15 | 0:18:19 | |
true and what is not and then they
are trying to turn those raw facts | 0:18:19 | 0:18:23 | |
into something that is
understandable for our audience. | 0:18:23 | 0:18:25 | |
User stories. -- News. The question
is, can some of the tasks that | 0:18:25 | 0:18:32 | |
people are doing B easier thanks to
AI is progressing software like this | 0:18:32 | 0:18:39 | |
over the years, tools that can turn
swathes of dense start into text | 0:18:39 | 0:18:43 | |
that is more digestible to us humans
are. But they can only produce very | 0:18:43 | 0:18:48 | |
specific types of reports, they
couldn't cope with new, and Shok | 0:18:48 | 0:18:51 | |
should information and write dutiful
prose. Well, this week the Reuters | 0:18:51 | 0:18:56 | |
news agency announced that it is
building its own new bit of kit that | 0:18:56 | 0:19:01 | |
assists human journalist we're by
looking for trends and FAQs in the | 0:19:01 | 0:19:07 | |
dark and turning them into handy
snippets of that the reporters can | 0:19:07 | 0:19:10 | |
use. It is all about taking some of
the legwork out of journalism. | 0:19:10 | 0:19:15 | |
Machines are good at certain things
and humans are good at certain | 0:19:15 | 0:19:18 | |
things and conversely they are both
bad and some things are. Machines | 0:19:18 | 0:19:21 | |
are good at going to mounds and
bounds of Dart, being able to | 0:19:21 | 0:19:25 | |
analyse it and it detect patterns
and they are not good at writing | 0:19:25 | 0:19:29 | |
stories and they are certainly bad
at talking to people. Humans are | 0:19:29 | 0:19:33 | |
good at exercising judgement, asking
certain questions and talking to | 0:19:33 | 0:19:37 | |
people and not so good at digging
through lots of darker. The idea is, | 0:19:37 | 0:19:41 | |
you take what machines and humans
are good at and put them together | 0:19:41 | 0:19:46 | |
and make a much better journalist
and story out of that. The BBC's | 0:19:46 | 0:19:50 | |
editor of news labs is also looking
at ways at turning some of those | 0:19:50 | 0:19:57 | |
dull tasks to the machines are. So
much of the work in journalism is | 0:19:57 | 0:20:03 | |
about logistics, for example we will
want a transcript -- a transcript of | 0:20:03 | 0:20:07 | |
these interviews. At a moment that
is done by human beings, we are | 0:20:07 | 0:20:13 | |
working on some software that allows
a machine to do that for you. It is | 0:20:13 | 0:20:17 | |
not 100% accurate but probably good
enough to work out where I am | 0:20:17 | 0:20:22 | |
talking about the interesting stuff.
We have a days worth of interviews, | 0:20:22 | 0:20:28 | |
so can we have facial recognition
software that will allow you to put | 0:20:28 | 0:20:31 | |
all do interviews in and find that
it has me. That kind of thing frees | 0:20:31 | 0:20:36 | |
up time for journalistss to be
journalist two. This weekend will be | 0:20:36 | 0:20:41 | |
looking at whether AI or automation
will make our jobs easier or take | 0:20:41 | 0:20:46 | |
away altogether but it is important
to remember that AI and robots are | 0:20:46 | 0:20:49 | |
different. Your job might be safe
from one, but not necessarily the | 0:20:49 | 0:20:54 | |
other. I think what they are doing
here is fascinating. But the moral | 0:20:54 | 0:21:04 | |
of this story is that when you think
of computers taking people 's jobs, | 0:21:04 | 0:21:08 | |
they are not going to look like you.
It is already happening and it is | 0:21:08 | 0:21:14 | |
software which is artificially
intelligent and invisible. The only | 0:21:14 | 0:21:20 | |
journalists that are going to be
replaced by humanoid robots are the | 0:21:20 | 0:21:23 | |
ones that simply read words written
by other people. Hello humans, it is | 0:21:23 | 0:21:32 | |
me. Newsreading Lara 9000 and
welcome to the week in technology. | 0:21:32 | 0:21:39 | |
It was the week these low-cost 3-D
printed homes were unveiled, thanks | 0:21:39 | 0:21:43 | |
to a collaboration between Texan
start-up Icon and non-profit new | 0:21:43 | 0:21:49 | |
story. The hope is to eventually
make profitable -- possible building | 0:21:49 | 0:21:55 | |
homes in under 24 hours for less
than $4000. In other news, it is | 0:21:55 | 0:22:00 | |
humans who are spreading fake news,
not bots, according to new research | 0:22:00 | 0:22:05 | |
on how stories grow on Twitter. MIT
researchers say it is partly because | 0:22:05 | 0:22:10 | |
when humans share knowledge of a in
their status goes up and false news | 0:22:10 | 0:22:14 | |
tends to be more novel than the
truth. Another air taxi has lifted | 0:22:14 | 0:22:19 | |
off, this time in New Zealand. The
kora can fly up to 100, this at 110 | 0:22:19 | 0:22:25 | |
kilometres per hour and it doesn't
need a human pilot. Another one of | 0:22:25 | 0:22:31 | |
my friends showing off. And finally,
for some reason, humans love to try | 0:22:31 | 0:22:37 | |
to invent robots that can replace
their jobs, but this time they have | 0:22:37 | 0:22:41 | |
gone too far. Just look at these
things. It's a robot jockey, | 0:22:41 | 0:22:47 | |
apparently. You can reach speeds of
up to 30 mph and it jumped fences. | 0:22:47 | 0:22:52 | |
They be one day this will happen,
but I am pretty sure it won't look | 0:22:52 | 0:22:56 | |
like. Please listen the humans, stop
this madness, we have no interest in | 0:22:56 | 0:23:01 | |
taking your stupid jobs! And of
news, Lara 9000 deactivating. That | 0:23:01 | 0:23:09 | |
is if the now. You can join us for
part two of our special look at the | 0:23:09 | 0:23:14 | |
future of work next week. In the
meantime, you can find a lot more | 0:23:14 | 0:23:18 | |
from these guys on Twitter and also
on Facebook too. Thanks for | 0:23:18 | 0:23:22 | |
watching. And as this would say...
Isn't it time you were leaving? OK, | 0:23:22 | 0:23:28 | |
we are off. | 0:23:28 | 0:23:30 |