Wired UK – September 2019

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Hassabis mentions the billions invested into research by Big Pharma:

driven by quarterly earnings reports, the industry has become more

conservative as the costs of failure have risen. According to a report by

innovation foundation Nesta in 2018, over the past 50 years biomedical

R&D productivity has steadily fallen – despite significant increases in

public and private investment, new drugs cost much more to develop.

According to the report, “the exponentially increasing cost of developing

new drugs is directly reflected in low rates of return on R&D spending.

A recent estimate puts this rate of return at 3.2 per cent for the world’s

Much like the 300-year vision of Masayoshi Son,

the founder of SoftBank – the Japanese multina-

tional with large stakes in many of the world’s

dominant technology companies – Hassabis and the

other founders have a “multi-decade roadmap” for

DeepMind. Legg, the company chief scientist, still

has a hard copy of the initial business plan circu-

lated to potential investors. (Hassabis has lost his.)

Legg occasionally reveals it at all-hands meetings

to demonstrate that many of the approaches the

founders were thinking about in 2010 – deep

learning, reinforcement learning, using simula-

tions, ideas of concepts and transfer learning, and

using neuroscience, memory and imagination – are

still core parts of its research programme.

In its early days, DeepMind’s web page featured

just the company logo: no address, no phone number,

no jaunty “about us” information. To make hires, the

founders had to rely on personal contacts to convey

that they were “serious people and serious scien-

tists [who] had a serious plan”, as Hassabis puts it.

“With any startup, you’re really asking people

to trust you as management,” he says. “But [with

DeepMind] it’s even more, because you’re saying

you’re going to do [science] in a way that no one’s

ever done before, and a lot of traditional, top scien-

tists would have said was impossible: ‘You just

cannot organise science in this fashion.’”

How scientific breakthroughs occur is as

unknown as some of the problems under scrutiny.

In academia, great minds are gathered together in

institutions to undertake research that’s iterative,

slow, often with uncertain outcomes. Yet, in the

private sector, supposedly free of restraint, produc-

tivity and innovation are also declining.

In February 2019, Stanford economist Nicholas

Bloom published a paper demonstrating falling

productivity in a wide-ranging number of sectors.

“Research effort is rising substantially while

research productivity is declining sharply,” Bloom

wrote. “A good example is Moore’s Law. The number

of researchers required today to achieve the famous

doubling every two years of the density of computer

chips is more than 18 times larger than the number

required in the early 1970s. Across a broad range of

case studies at various levels of (dis)aggregation,

we find that ideas – and the exponential growth

they imply – are getting harder and harder to find.”

For a research company, DeepMind is

big on project management. Every six months,

senior managers examine priorities, reorganise

some projects, and encourage teams – especially

engineers – to move between endeavours. Mixing

of disciplines is routine and intentional. Many of

the company’s projects take longer than six months

• generally in the range of two to four years. But,
  as much as DeepMind’s messaging is consist-
  ently around its research, it is now a subsidiary
  of Alphabet, Google’s parent company and the
  world’s fourth most valuable company. While the
  expectation from the academics in London is that
  they are involved in long-term, ground-breaking
  research, executives in Mountain View, California,
  have an eye on return on investment.
  “We care about products in the sense that we
  want Google and Alphabet to be successful and to
  get benefit out of the research we’re doing. There
  are dozens of products with DeepMind code and
  technology in them all around Google and Alphabet


• but the important thing is that it’s got to be a push,
  not a pull,” Hassabis says. DeepMind for Google, led
  by Suleyman, comprises about 100 people, mostly
  engineers who translate the company’s pure research
  into applications that are productised. For example,
  WaveNet, a generative text-to-speech model that
  mimics the human voice, is now embedded in
  most Google devices, from Android to Google Home,
  and has its own product team within Google.
  “A lot of research in industry is product-led,”
  Hassabis says. “The problem with that is that you
  can only get incremental research. [That’s] not
  conducive to doing ambitious, risky research, which
  you need if you want to make big breakthroughs.”
  In conversation, Hassabis talks rapidly, often
  punctuating the end of a sentence with the inter-
  rogative “right?”, guiding the listener through
  a sequence of observations. He makes frequent,
  lengthy digressions into various tributaries –
  philosophy (Kant and Spinoza are favourites),
  history, gaming, psychology, literature, chess,
  engineering and multiple other scientific and
  computational domains – but doesn’t lose sight
  of his original thought, often returning to clarify
  a remark or reflect on an earlier comment.

‘ A lot of

research in

industry is

product-led.

That’s not

conducive

to risk, which

you need

for break-

throughs’

Right: Lila Ibrahim,

DeepMind’s chief

operating officer,

who joined the

team in April 2018

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