Episode 48: Build. Test. Repeat

This episode features our pals Ali Afshar and Ignacio Willats founders of Hackscience, a startup focussed on streamlining research by taking time consuming lab tasks out of hands of the scientists through automation. Their principle product currently is the cell feed exchanger, which replenishes the liquid food required for healthy cells in culture. This process can take hours out the day, and often requires the researcher to come in during weekends. Automating research also allows for science that is reproducible, and it seems that the future of biology is machine readable.

HackScience has its origins as a hackathon, with Ali keen to collide scientists and engineers to solve research problems, and Ignacio being well versed in hackathons, and startup development. The interdisciplinary nature of the hackathon nicely represents the constructive, collaborative nature of the ideal research environment, exposing two often isolated groups with the skillsets, and problem sets of the other.

Rapid prototyping is in the DNA of HackScience. This is characteristic of hackathons, but when combined with Co-Founder Ignacio's drive to always stay on top of the demands of researchers and iterate accordingly, has resulted in HackScience losing sight of the core mission which is to actually help researchers. 

One thing that was particularly interesting to hear about was Ali's dual life as a startup founder, and as an active PhD Researcher at Imperial College. We've spoken to researchers that have developed their startups as PhD's in their spare time (Episode 13 - Mark Hahnel), and founders who in order to really make a go of the company felt that leaving academia made the most sense (Episode 46 - Bethan Wolfenden). But Ali is operating under an exceptional set of circumstances. Working three days a week at Imperial developing the science behind printable solar cells and developing HackScience every hour of every other day. The balancing act is impressive, however it seemed Ali would not have it any other way, with the rapidity of startup life as a kind of hectic respite from the slow plod of research.

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* If you liked the episode be sure to subscribe on iTunes (or your podcast site of choice) and leave a rating/comment. It helps a bunch :)

** Other podcasts on similar topics:

Episode 28: Science's Mission Control, with Alok Tayi Founder  of TetraScience

Episode 46: From Side Project to Startup, with Bethan Wolfenden Co-Founder of BentoBio

Linda’s Top Picks From Hello Tomorrow

Linda’s Top Picks From Hello Tomorrow

As a first-timer, I was awe-struck by the ingenuity of many of the projects, ranging from hibernation for long-haul space travel to rejuvenating aging cells to creating new materials. It was hard to whittle it down, but here are my three favourite companies the summit.

Episode 45: Securing the Future of Food

In this episode we chatted to Christine Gould, founder and CEO of the Thought for Food Foundation. Their annual conference, startup challenge and active community centres around the science and tech working to ensure we have enough food to feed the world. 

With Christine, we talked about how to bring together diverse groups of people - startups, scientists, designers, policy makers, corporates and, in particular, young people, to work towards solutions. She explained how the TFF annual summit is centred around experience design and a strong culture of innovation (openness, collaboration, beginner's mindset, entrepreneurial methods, purpose before paycheck and larger-than-life energy), and that this can be replicated across sectors. 

Christine was particularly passionate about how young people can build and design the future, and how critical their involvement is. 

We were particularly interested in Christine's attitude towards agriculture in 2017 being a place ripe for tech and science innovation, and hence, one of the most exciting sectors to be focusing on right now!

Episode 44: Leading the Automation Revolution

In this episode we chatted to Kristin Ellis, the Scientific Development Lead at OpenTrons, about all things science. OpenTrons is a company that builds affordable open-source lab robots, that remove the need to perform tedious manual pipetting tasks, to free up valuable time for researchers. 

We touched on the importance of good science communication and the unfair stigma that often impacts researchers that are keen to involve and talk to the public, and the true value of encouraging that "...and then it just clicks" moment with people previously disengaged with science. 

We also spoke about the innovative ways tinkerers have adapted their open-source robots, the value of putting automation into the hands of the many, and the attitude shift required in science to promote prototyping and hacking. We were keen to see how OpenTrons has been received by academics looking to streamline their research and were fascinated by their passage through Haxclr8tr (a hardware startup accelerator, now called HAX). Their relationship to Shenzhen is also pretty amazing - described as the silicon valley for hardware, the labyrinthine market in Shenzhen allows hardware hackers to rapidly test out ideas, a concept essentially intractable even with the electronic hardware superstores elsewhere.

Episode 43: Getting to Science 2.0

In this episode Tim O'Reilly (CEO of O'Reilly Media) joins us in a far reaching conversation spanning the whole science ecosystem. From the communication of science, to liberating knowledge generated by research from the confines of the static PDF, to the mutual learning experience of colliding technologists and academics,

Tim has been regarded as a thought leader in Silicon Valley over the past few decades, popularising the terms open source and web 2.0. So we were interested to see how he believes the rapid technological advancement of late could impact science and academic culture.. 

O'Reilly Media also operates an awesome conference called SciFoo. The event is a partnership between O'Reilly, Google, Digital Science, and the Nature Publishing Group which brings together an interdisciplinary cohort of scientists, as well as technologists and policy makers, so it was great to hear how Tim feels collaboration can be done in the 21st century. 

Episode 41: Taking Action in Science

This episode we speak to Elizabeth Iorns who is the Founder and CEO of Science Exchange. We wanted to get Elizabeth's view on what it really takes change the status quo in science - both from a process perspective in the way we conduct ourselves in a lab with regards to suppliers, but also from an activation standpoint - instead of training people up on reproducibility, actually going out and making change using the resources she had access to. We are all about finding role models for change at Science: Disrupt, and Elizabeth is a perfect example of someone who takes action and builds the future - making scientific revolution seem just that bit more achievable.
 

Episode 39: Imogen Bunyard

This episode we spoke to Imogen Bunyard, CoFounder of Qadre a startup focussed on building blockchain solutions that tackle trust issues within enterprises. This could include tackling the counterfeit drug market. Imogen has a particular knack for breaking down a complex topic (in this case the blockchain), grounding it reality, and imagining use cases that can really make a difference.

There's a lot of hype and plenty of misinformation around blockchain, it's either the domain of drug smugglers and the dark web; or like AI, it's presumed that it's a silver bullet for every company woe you can imagine. There's also little effort made to make the topic actually understandable, with convoluted analogies galore

We were keen to hear about the paucity of academic research in the field as researchers are drawn away from the academy. There's plenty of articles on the idea of brain drain, as corporates look to build up their intellectual inventory by essentially buying up scientists (sometimes even entire labs). Research skills in data science, computer vision, or AI are incredibly lucrative propositions for organisations, and the big tech companies are hoovering up research departments left and right but at least those fields had time to become established as topics of study within most universities.

Episode 38: Open Minds, Open Hardware

This episode was recorded in the bowels of Sussex University when we met up with Tom Baden a Neuroscientist interested in how the visual system processes information. Our motivation for chatting to Tom was a brilliant project called the FlyPi that he developed, along with Andre Chagas another Neuroscientist who joined us via the magic of Skype.

FlyPi is a great representation of a seemingly growing phenomena of DIY tools within the labs - you can read the paper for the specs, but in short it's a 3D printed lab for imaging experiments - specifically of the fruit fly (as the name FlyPi might suggest). Along with the FoldScope, and a number of other simple, cheap tools (including a fidget spinner centrifuge ...), the ability to probe the natural world in a meaningful way is being made available to a much wider audience.  

We spoke a bunch about Tom's Trend in Africa programme, which trains up researchers in underserved parts of the continent so they're up to scratch with the latest neuroscience tools/knowhow. We also discussed the broad topic of the maker movement in biology, the fear of experimenting with experiments, and the way that DIY hardware in science needs to be shown off in the appropriate venues (and that means not just buried away in the academic literature).

We thoroughly enjoyed chatting to Andre and Tom, and we left feeling energised that the spirit of ingenuity, of tinkering, and playing with science is alive and well.

Episode 37: Science in Seattle

This episode we speak to Zach Mueller, an Amazon Data Scientist and co-Founder of Sound.Bio, Seattle's first DIY Biohackspace. We wanted to hear about how they aim to build a community around biology, the challenges of setting up the lab, and the efforts they go to to educate Seattleites in modern biotech.

Zach comes to biology with little experience, in fact he was drawn to the field after listening to a podcast that spoke about IGEM, the synthetic biology competition for undergraduate teams. This idea of arriving at the lab with a minimal background in the science, is what these biohackspaces are all about. They're a place where you can experiment with experimenting, learn new skills, and join a community that is committed to producing value through biotech.

The space itself is kitted out with the kinds of tools you would expect in order to carry out modern biology experiments. However, the lab is also keen to leverage the skills and resourcefulness of the maker community, to really hammer home the important concept that biology doesn't have to be restricted to the confines of a university. Or perhaps more importantly, that participating in biology is not simply the reserve of institutions with pockets deep enough to purchase the latest tech.

Darktrace: Cybersecurity's AI Counterpuncher

How can we protect against the cyber threats of today and prepare for the threats of tomorrow? Darktrace are a rapidly growing tech company based out of San Francisco and Cambridge, UK who are getting well deserved attention with their novel approaches to both AI and cybersecurity. Founded through a collision of academic mathematicians and members of the security services who had been tackling state level attacks, Darktrace takes a very different approach to how cyber-attacks can be defended against. Speaking to Darktrace CMO Emily Orton, we got some insight into the challenges facing enterprises in todays interconnected world.

Darktrace is taking 21st century cyber approaches to defend enterprise infrastructure.

New advances in cyber-security are realistically legacy approaches whereby they rely on knowing who we’re protecting against and defending the perimeter from them. Orton highlighted that this approach is the same as building a moat around a Tuscan Castle: it’s not sufficient to protect us against modern techniques of infiltration. The issue today is that networks are too large and interconnected – there’s simply too many in-roads into a network for the ‘walled off’ style approach to be sustainable – as such, Darktrace are trying something different. The standard dogma of cybersecurity is that threats can be kept outside of the network. This has meant that the bulk of effort in the security sector and the IT industry at large, is based on the simplistic idea that we can just keep the bad guys out. However, this worldview historically hasn’t adjusted for defence against actors within the network itself.

Darktrace, while grounded in the heady mathematics of Machine Learning and Bayesian Probability Theory has taken inspiration from the Life Sciences. Specifically, in the way the Immune System operates. Our surface barrier, the skin, protects us from pathogens and foreign bodies. The ability to venture out into the world without fear of contracting an infection through simple contact is not something many are concerned about, and this is down to the efficacy of this barrier. However, the skin on its own is insufficient to produce a robust defence of the host – internal security mechanisms must be active. So, with this ‘bioinspiration’, Darktrace aims to catch anomalies from within the network, in what they term the ‘Enterprise Immune System’.

Machine Learning allows an enterprise to be alerted to unusual behaviour that would have previously gone unnoticed such as an insider syphoning off files, especially if that user isn’t already on some comprehensive watchlist. But Darktrace aren’t typically bumping up against malicious agents infiltrating some enterprise, interestingly much of their work in this context has to do with a technological naivety found in employees and their regard for basic data security. Orton pointed to a few groan-worthy examples of everyday behaviour that would stump security in the traditional paradigm: “We had a user sending massive files using iMessage because he couldn’t get them out. His company was so locked down. From what we could see he wasn’t malicious, he just wanted to get his work done. We’ve had game developers sending source code back on their Gmail accounts, these aren’t people that are trying to take the company down, again they’re just trying to do their work. Sometimes people just make mistakes, we’re seeing ransomware cases every week, and that’s always someone who clicked on links. And those phishing attacks are getting better and better”

There’s a general resignation in cybersecurity that as the creation of new threats occurs, awareness of those threats lags significantly. Orton noted that threats like polymorphic malware, are particularly problematic because they won’t appear in the security blogs until the malware has already undergone several ‘iterations’. The importance of automation now comes to the fore: action has to be taken in real time. The days of pushing software patches on the order of months after an infiltration are over.

It’s not simply in the defence of the network that Darktrace is applying their knowhow. Darktrace has proven to be an effective counter-puncher and bring to life the Immune System analogy with the company’s Antigena. This is an algorithm based counterstrike service that can intercept malicious actions before they can damage your core infrastructure and data. The cool thing about this is that, in essence, it’s the first time machine learning is responding to threats as opposed to just passive detection.  As Orton intimated, “We now have a portion of our customers that love the machine learning so much, and have seen it in practice, that they trust that machine to take action on their behalf.” This highlights a larger point that there’s simply too many threats, and even with a 24/7 security team – there’s a capacity to what humans can deal with, and the volume of threats becomes an unmanageable problem when it comes to protecting your assets. She touched on the idea that automated cyber warfare is a likely reality, with automated hackers, tackling AI defence systems. But there’s an advantage to being on the defensive side, being proactive and assuming there will be attempts at infiltration: by learning everything you can about your network, you know the battlefield better than any attacker.

Darktrace customers discuss the Enterprise Immune System's unique approach to cyber defense.

Machine Learning and Bayesian Stats are nothing new, so why has it taken so long for the likes of Darktrace to come along? Processing power certainly has a hand in that. But again, the scale of the networks and sheer difference between the network architecture of one enterprise to another makes this an unusual and previously intractable challenge: if you’re at a media agency, Facebook may be whitelisted; walk into a bank and these sites will be firmly on the blacklist. Additionally, you can include remote working and freelancing to the complexity.

In order to handle the variability in the threats, various algorithms are employed which are then filtered for performance. Darktrace are tracking many events, with automation allowing them to probe hundreds of metrics of behavior. These could include the login time stamps, volume of data transfer, and the frequency of data transfer. Perhaps a user logs into their computer at 2am, this may be unusual, but not something that would pass the threshold in order to be flagged to the security personnel. However, if they also see the connection was to somewhere in Ukraine, a pattern of behaviour not previously seen in the user history, and perhaps the volume of data being transferred far exceeded your typical usage, then that would be flagged. The enterprise itself makes the decision on how sensitive they are to these flags – a financial institution will have a lower threat tolerance than another for example.

All users, the devices they use, and the network as a whole are all modelled, and as such Darktrace are learning what’s normal for that user, that device, that network. However, this data set is not producing a static baseline of events, the system Darktrace is deploying is calculating probabilities based on evolving evidence.

After all, if the threats are constantly innovating, surely our defence should be too?