Thirty five years ago having a PhD in computer vision was
considered the height of unfashion, as artificial intelligence
languished at the bottom of the trough of disillusionment.

Back then it could take a day for a computer vision algorithm
to process a single image. How times change.

“The competition for talent at the moment is absolutely
ferocious,” agrees Professor Andrew Blake, whose computer
vision PhD was obtained in 1983, but who is now, among other
things, a scientific advisor to UK-based autonomous vehicle
software startup, FiveAI, which is aiming
to trial driverless cars on London’s roads in 2019.

Blake founded Microsoft’s computer vision group, and was
managing director of Microsoft Research, Cambridge, where
he was involved in the development of the Kinect sensor — which
was something of an augur for computer vision’s rising star
(even if Kinect itself did not achieve the kind of consumer
success Microsoft might have hoped).

He’s now research director at the Alan Turing
in the UK, which aims to support data science
research, which of course means machine learning and AI, and
includes probing the ethics and societal implications of AI and
big data.

So how can a startup like FiveAI hope to compete with tech
giants like Uber and Google, which are also of course working
on autonomous vehicle projects, in this fierce fight for AI

And, thinking of society as a whole, is it a risk or an
opportunity that such powerful tech giants are throwing
everything they’ve got at trying to make AI breakthroughs?
Might the AI agenda not be hijacked, and progress in the field
monopolized, by a set of very specific commercial agendas?

“I feel the ecosystem is actually quite vibrant,” argues Blake,
though his opinion is of course tempered by the fact he was
himself a pioneering researcher working under the umbrella of a
tech giant for many years. “You’ve got a lot of talented people
in universities and working in an open kind of a way — because
academics are quite a principled, if not even a cussed bunch.”

Blake says he considered doing a startup himself, back in 1999,
but decided that working for Microsoft, where he could focus on
invention and not have to worry about the business side of
things, was a better fit. Prior to joining Microsoft his
research work included building robots with vision systems that
could react in real time — a novelty in the mid-90s.

“People want to do it all sorts of different ways. Some people
want to go to a big company. Some people want to do a startup.
Some people want to stay in the university because they love
the productivity of having a group of students and postdocs,”
he says. “It’s very exciting. And the freedom of working in
universities is still a very big draw for people. So I don’t
think that part of the ecosystem is going away.”

Yet he concedes the competition for AI talent is now at fever
pitch — pointing, for example, to startup Geometric
, founded by a group of academics
and acquired by Uber at the end of 2016 after
operating for only about a year.

“I think it was quite a big undisclosed sum,” he says of the
acquisition price for the startup. “It just goes to show how
hot this area of invention is.

“People get together, they have some great ideas. In that case
instead of writing a research paper about it, they decided to
turn it into intellectual property — I guess they must have
filed patents and so on — and then Uber looks at that and
thinks oh yes, we really need a bit of that, and Geometric
Intelligence has now become the AI department of Uber.”

Blake will not volunteer a view on whether he thinks it’s a
good thing for society that AI academic excellent is being so
rapidly tractor-beamed into vast, commercial motherships. But
he does have an anecdote that illustrates how conflicted the
field has become as a result of a handful of tech giants
competing so fiercely to dominate developments.

“I was recently trying to find someone to come and consult for
a big company — the big company wants to know about AI, and it
wants to find a consultant,” he tells TechCrunch. “They wanted
somebody quite senior… and I wanted to find somebody who didn’t
have too much of a competing company allegiance. And, you know
what, there really wasn’t anybody — I just could not find
anybody who didn’t have some involvement.

“They might still be a professor in a university but they’re
consulting for this company or they’re part time at that
company. Everybody is involved. It is very exciting but
the competition is ferocious.”

“The government at the moment is talking a lot about AI and the
context of the industrial strategy and understanding that it’s
a key technology for productivity of the nation — so a very
important part of that is education and training. How are we
going to create more excellence?” he adds.

The idea for the Turing Institute, which was set up in 2015 by
five UK universities, is to play a role here, says Blake, by
training PhD students, and via its clutch of research fellows
who, the hope is, will help form the next generation of
academics powering new AI breakthroughs.

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“The big breakthrough over the last ten years has been deep
learning but I think we’ve done that now,” he argues. “People
are of course writing more papers than ever about it. But it’s
entering a more mature phase where at least in terms of using
deep learning. We can absolutely do it. But in terms of
understanding deep learning — the fundamental mathematics of it
— that’s another matter.”

“But the hunger, the appetite of companies and universities for
trained talent is absolutely prodigious at the moment — and I
am sure we are going to need to do more,” he adds, on education
and expertise.

Returning to the question of tech giants dominating AI research
he points out that many of these companies are making public
toolkits available, such as Google, Amazon and Microsoft have
done, to help drive activity across a wider AI ecosystem.

Meanwhile academic open source efforts are also making
important contributions to the ecosystem, such as Berkley’s
deep learning framework, Caffe. Blake’s view therefore is
that a few talented individuals can still make waves —
despite not wielding the vast resources of a Google, an Uber or
a Facebook.

“Often it’s just one or two people — when you get just a couple
of people doing the right thing it’s very agile,” he says.
“Some of the biggest advances in computer science have come
that way. Not necessarily the work of a group of a hundred
people. But just a couple of people doing the right thing.
We’ve seen plenty of that.”

“Running a big team is complex,” he adds. “Sometimes, when you
really want to cut through and make a breakthrough it comes
from a smaller group of people.”

That said, he agrees that access to data — or, more
specifically “the data that relates to your problem”, as he
qualifies it — is vital for building AI algorithms. “It’s
certainly true that the big advance over the last ten years has
depended on the availability of data — often at
Internet-scale,” he says. “So we’ve learnt, or we’ve
understood, how to build algorithms that learn with big data.”

And tech giants are naturally positioned to feed off of their
own user-generated data engines, giving them a built-in
reservoir for training and honing AI models — arguably locking
in an advantage over smaller players that don’t have, for
example in Facebook’s case, billions of users generating
data-sets on a daily basis.

Although even Google, via its AI division DeepMind, has felt
the need to acquire certain high value data-sets by forging
partnerships with third party institutions —
such as the UK’s National Health Service, where DeepMind Health
has, since late 2015, been accessing millions of people’s medical data, which the
publicly funded NHS is custodian of, in an attempt to build AIs
that have diagnostic healthcare benefits.

Even then, though, the vast resources and high public profile
of Google appears to have given the company a leg up. A smaller
entity approaching the NHS with a request for access to
valuable (and highly sensitive) public sector healthcare data
might well have been rebuffed. And would certainly have been
less likely to have been actively invited in, as DeepMind says
it was. So when it’s Google-DeepMind offering ‘free’ help to
co-design a healthcare app or their processing resources and
expertise in exchange for access to data, well, it’s
demonstrably a different story.

Blake declines to answer when asked whether he thinks DeepMind
should have released the names of the people on its AI ethics board. (“Next question!”) Nor will
he confirm (nor deny) if he is one of the people sitting on
this anonymous board. (For more on his thoughts on AI and
ethics see the additional portions from the interview at the
end of this post.)

But he does not immediately subscribe to the view that AI
innovations must necessarily come at the cost of individual
privacy — as some have suggested by, for example, arguing that
Apple is fatally disadvantaged in the AI race because it will
not data-mine and profile its users in the no-holes-barred
fashion that a Google or a Facebook does (Apple has rather
opted to perform local data processing and apply obfuscation
techniques, such as differential privacy, to offer is users AI
smarts that don’t require they hand over all their

Nor does Blake believe AI’s blackboxes are fundamentally
unauditable — a key point given that algorithmic accountability will surely be
necessary to ensure this very powerful technology’s societal
impacts can be properly understood and regulated, where
necessary, to avoid bias being baked in. Rather he says
research in the area of AI ethics is still in a relatively
early phase.

“There’s been an absolute surge of algorithms — experimental
algorithms, and papers about algorithms — just in the last year
or two about understanding how you build ethical principles
like transparency and fairness and respect for privacy into
machine learning algorithms and the jury is not yet out. I
think people have been thinking about it for a relatively short
period of time because it’s arisen in the general consciousness
that this is going to be a key thing. And so the work is
ongoing. But there’s a great sense of urgency about it because
people realize that it’s absolutely critical. So we’ll have to
see how that evolves.”

On the Apple point specifically he responds with a “no I don’t
think so” to the idea that AI innovation and privacy might be
mutually exclusive.

“There will be good technological solutions,” he continues.
“We’ve just got to work hard on it and think hard about it —
and I’m confident that the discipline of AI, looked at broadly
so that’s machine learning plus other areas of computer science
like differential privacy… you can see it’s hot and people are
really working hard on this. We don’t have all the answers yet
but I’m pretty confident we’re going to get good answers.”

Of course not all data inputs are equal in another way when it
comes to AI. And Blake says his academic interest is especially
piqued by the notion of building machine learning systems that
don’t need lots of help during the learning process in order to
be able to extract useful understandings from data, but rather
learn unsupervised.

“One of the things that fascinates me is that humans learn
without big data. At least the story’s not so simple,” he says,
pointing out that toddlers learn what’s going on in the world
around them without constantly being supplied with the names of
the things they are seeing.

A child might be told a cup is a “cup” a few times, but not
that every cup they ever encounter is a “cup”, he notes. And if
machines could learn from raw data in a similarly lean way it
would clearly be transformative for the field of AI. Blake sees
cracking unsupervised learning as the next big challenge for AI
researchers to grapple with.

“We now have to distinguish between two kinds of data — there’s
raw data and labelled data. [Labelled] data comes at a high
price. Whereas the unlabelled data which is just your
experience streaming in through your eyes as you run through
the world… and somehow you still benefit from that, so there’s
this very interesting kind of partnership between the labelled
data — which is not in great supply, and it’s very expensive to
get — and the unlabelled data which is copious and streaming in
all the time.

How do we make the best use of a very limited supply of
expensively labelled data? Understanding that labelled data
is in very short supply — and privileging the labelled data.
How are we going to get the algorithms that flourish in that

“And so this is something which I think is going to be the big
challenge for AI and machine learning in the next decade — how
do we make the best use of a very limited supply of expensively
labelled data?”

“I think what is going to be one of the major sources of
excitement over the next five to ten years, is what are the
most powerful methods for accessing unlabelled data and
benefiting from that, and understanding that labelled data is
in very short supply — and privileging the labelled data. How
are we going to do that? How are we going to get the algorithms
that flourish in that environment?”

Autonomous cars would be one promising AI-powered technology
that obviously stands to benefit from a breakthrough on this
front — given that human-driven cars are already being equipped
with cameras, and the resulting data streams from cars being
driven could be used to train vehicles to self drive if only
the machines could learn from the unlabelled data.

FiveAI‘s website suggests this goal is also on
its mind — with the startup saying it’s using “stronger AI” to
solve the challenge of autonomous vehicles safely navigating
complex urban environments, without needing to have
“highly-accurate dense 3D prior maps and localization”. A
challenge billed as being “defined as the top level in autonomy
– 5”.

“I’m personally fascinated with how different it is humans
learn from the way, at the moment, our machines are learning,”
adds Blake. “Humans are not learning all the time from big
data. They’re able to learn from amazingly small amounts of

He cites research by MIT’s Josh Tenenbaum showing
how humans are able to learn new objects after just one or two
exposures. “What are we doing?” he wonders. “This is a
fascinating challenge. And we really, at the moment, don’t know
the answer — I think there’s going to be a big race on,
from various research groups around the world, to see and to
understand how this is being done.”

He speculates that the answer to pushing forward might lie in
looking back into the history of AI — at methods such as
reasoning with probabilities or logic, previously applied
unsuccessfully, given they did not result in the breakthrough
represented by deep learning, but which are perhaps worth
revisiting to try to write the next chapter.

“The earlier pioneers tried to do AI using logic and it
absolutely didn’t work for a whole lot of reasons,” he says.
“But one property that logic seems to have, and perhaps we can
somehow learn from this, is this idea of being incredibly
efficient — incredibly respectful if you like — of how costly
the data is to acquire. And so making the very most of even one
piece of data.

“One of the properties of learning with logic is that the
learning can happen very, very quickly, in the sense of only
needing one or two examples.”

It’s a nice idea that the hyper fashionable research field of
AI, as it now is, where so many futuristic bets are being
placed, might need to look backwards, to earlier apparent
dead-ends, to achieve its next big breakthrough.

Though, given Blake describes the success of deep networks as
“a surprise to pretty much the whole field” (i.e. that the
technology “has worked as well as it has”) it’s clear that
making predictions about the forward march of AI is a tricky,
possibly counterintuitive business.

As our interview winds up I hazard one final thought — asking
whether, after more than three decades of research in
artificial intelligence, Blake has come up with his own
definition of human intelligence?

“Oh! That’s much too hard a question for the final question of
the interview,” he says, punctuating this abrupt conclusion
with a laugh.

On why deep learning is such a black box
suppose it’s sort of like an empirical finding. If you think
about physics — the way experimental physics goes and
theoretical physics, very often, some discovery will be made in
experimental physics and that sort of sets off the theoretical
physics for years trying to understand what was actually
happening. But the way you first got there was with this
experimental observation. Or maybe something surprising. And I
think of deep networks as something like that — it’s a surprise
to pretty much the whole field that it has worked as well as it
has. So that’s the experimental finding. And the actual object
itself, if you like, is quite complex. Because you’ve got all
of these layers… [processing the input] and that happens maybe
ten times… And by the time you’ve put the data through all of
those transformations it’s quite hard to say what the composite
effect is. And getting a mathematical handle on all of that
sequence of operations. A bit like cooking, I suppose.”

On designing dedicated hardware for processing
“Intel build the whole processor and also they
build the equipment you need for an entire data center so
that’s the individual processors and the electronic boards that
they sit on and all the wiring that connects these processors
up inside the data center. The wiring actually is more than
just a bit of wire — they call them an interconnect. And it’s a
bit of smart electronics itself. So Intel has got its hands on
the whole system… At the Turing Institute with have a
collaboration with Intel… and with them we are asking exactly
that question: if you really have got freedom to design the
entire contents of the data center how can you build the data
center which is best for data science?… That really means, to a
large extent, best for machine learning… The supporting
hardware for machine learning is definitely going to be a key

On the challenges ahead for autonomous
“One of the big challenges in autonomous
vehicles is it’s built on machine learning technologies which
are — shall we say – “quite” reliable. If you read machine
learning papers, an individual technology will often be right
99% of the time… That’s pretty spectacular for most machine
learning technologies… But 99% reliability is not going to be
nearly enough for a safety critical technology like autonomous
cars. So I think one of the very interesting things is how you
combine… technologies to get something which, in the aggregate,
at the level of assist, rather than the level of an individual
algorithm, is delivering the kind of very high reliability that
of course we’re going to demand from our autonomous transport.
Safety of course is a key consideration. All of the engineering
we do and the research we do is going to be building around the
principle of safety — rather than safety as an afterthought or
a bolt-on, it’s got to be in there right at the beginning.”

On the need to bake ethics into AI
“This is something the whole field has
become very well tuned to in the last couple of years, and
there are numerous studies going on… In the Turing Institute
we’ve got a substantial ethics program where on the one hand
we’ve got people from disciplines like philosophy and the law,
thinking about how ethics of algorithms would work in practice,
then we’ve also got scientists who are reading those messages
and asking themselves how do we have to design the algorithms
differently if we want them to embody ethical principles. So I
think for autonomous driving one of the key ethical principles
is likely to be transparency — so when something goes wrong you
want to know why it went wrong. And that’s not only for
accountability purposes. Even for practical engineering
purposes, if you’re designing an engineering system and it
doesn’t perform up to scratch you need to understand which of
the many components is not pulling its weight, where do we need
to focus the attention. So it’s good from the engineering point
of view, and it’s good from the public accountability and
understanding point of view. And of course we want the public
to feel — as far as possible — comfortable with these
technologies. Public trust is going to be a key element. We’ve
had examples in the past of technologies that scientists have
thought about that didn’t get public acceptability immediately
— GM crops was one — the communication with the public wasn’t
sufficient in the early days to get their confidence, and so we
want to learn from those kinds of things. I think a lot of
people are paying attention to ethics. It’s going to be

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