…it is fantastic that Babylon has undertaken this evaluation, and has sought to present it in public via this conference paper. They are to be applauded for that. One of the benefits of going public is that we can now provide feedback on the study’s strength and weaknesses.
There’s been a lot of coverage of Babylon Health recently, with the associatedcontroversyaround what this might mean for GPs and patients. However, what might be even more interesting than the claim that a chatbot could replace a GP, is the fact that Babylon is one of the few companies that have published some of their work openly. This is quite unusual in an industry where startups are reluctant to share their methods for fear of exposing their “secret sauce”. But, as the open review by Enrico Coiera demonstrates, publication of methods for peer review and scientific scrutiny is an essential aspect of moving the field of clinical AI forward.
…nations that have begun to prepare for and explore AI will reap the benefits of an economic boom. The report also demonstrates how anyone who hasn’t prepared, especially in developing nations, will be left behind… In the developing world, in the developing countries or countries with transition economies, there is much less discussion of AI, both from the benefit or the risk side.
The growing divide between nations that are prepared for widespread automation and those that aren’t, between companies that can cut costs by replacing workers and the newly unemployed people themselves, puts us on a collision course for conflict and backlash against further developing and deploying AI technology
A short post that’s drawn mainly from the 64 page McKinsey report (Notes From the Frontier: Modeling the Impact of AI on the World Economy). This is something that I’ve tried to highlight when I’ve talked about this technology to skeptical colleagues; in many cases, AI in the workplace will arrive as a software update and will, therefore, be available in developing, as well as developed countries. This isn’t like buying a new MRI machine where the cost is in the hardware and ongoing support. The existing MRI machine will get an update over the internet and from now on it’ll include analysis of the image and automated reporting. And now the cost of running your radiology department at full staff capacity is starting to look more expensive than it needs to be. This says nothing of the other important tasks that radiologists perform; the fact is that a big component of their daily work includes classifying images, and for human beings, that ship has sailed. While in more developed economies it may be easier to relocate expertise within the same institution, I don’t think we’re going to have that luxury the developing world. If we’re not thinking about these problems today, we’re going to be awfully unprepared when that software update arrives.
An interesting (and sane) conversation about the defeat of AlphaGo by AlphaGo Zero. It almost completely avoids the science-fiction-y media coverage that tends to emphasise the potential for artificial general intelligence and instead focuses on the following key points:
Go is a stupendously difficult board game for computers to play but it’s a game in which both players have total information and where the rules are relatively simple. This does not reflect the situation in any real-world decision-making scenario. Correspondingly, this is necessarily a very narrow definition of what an intelligent machine can do.
AlphaGo Zero represents an order of magnitude improvement in algorithmic modelling and power consumption. In other words, it does a lot more with a lot less.
Related to this, AlphaGo Zero started from scratch, with humans providing only the rules of the game. So Zero used reinforcement learning (rather than supervised learning) to figure out the same (and in some cases, better) moves than human beings have done over the last thousand years or so).
It’s an exciting achievement but shouldn’t be conflated with any significant step towards machine intelligence that transfers beyond highly constrained scenarios.
A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.
You didn’t need to know about how to print on a printing press in order to read a printed book. Writing implements were readily available in various forms in order to record thoughts, as well as communicate with them. The use was simple requiring nothing more than penmanship. The rapid advancement of technology has changed this. Tech has evolved so quickly and so universally in our culture that there is now literacy required in order for people to effectively and efficiently use it.
Reading and writing as a literacy was hard enough for many of us, and now we are seeing that there is a whole new literacy that needs to be not only learned, but taught by us as well.
I wrote about the need to develop these new literacies in a recent article (under review) in OpenPhysio. From the article:
As clinicians become single nodes (and not even the most important nodes) within information networks, they will need data literacy to read, analyse, interpret and make use of vast data sets. As they find themselves having to work more collaboratively with AI-based systems, they will need the technological literacy that enables them to understand the vocabulary of computer science and engineering that enables them to communicate with machines. Failing that, we may find that clinicians will simply be messengers and technicians carrying out the instructions provided by algorithms.
It really does seem like we’re moving towards a society in which the successful use of technology is, at least to some extent, premised on your understanding of how it works. As educators, it is incumbent on us to 1) know how the technology works so that we can 2) help students use it effectively while at the same time avoid exploitation by for-profit companies.
See also: Aoun, J. (2017). Robot proof: Higher Education in the Age of Artificial Intelligence. MIT Press.
For all the promise that digital records hold for making the system more efficient—and the very real benefit these records have already brought in areas like preventing medication errors—EMRs aren’t working on the whole. They’re time consuming, prioritize billing codes over patient care, and too often force physicians to focus on digital recordkeeping rather than the patient in front of them.
I’ve read some physicians can spend up to 60% of their day capturing patient information in the EHR. And this isn’t because there’s a lot of information. It’s often down to confusing user interfaces, misguided approaches to security (e.g. having to enter multiple different passwords and a lack of off-site access), and poor design that results in physicians capturing more information than necessary.
There’s interest in using natural language processing to analyse recorded conversation between clinicians and colleagues/patients and while the technology is still unsuitable for mainstream use, it seems likely that it will continue improving until it is.
This has led companies interested in applying AI to healthcare to find different ways to scoop up as much data as possible. Google partnered with Stanford and Chicago university hospitals to collect 46 billion data points on patient visits. Verily, also owned by Google’s parent company Alphabet, is recruiting 10,000 people for its own long-term health studies. IBM has spent the last few years buying up health companies for their data, accumulating records on more than 300 million people.
I’ve pointed to this problem before; it’s important that we have patient data repositories that are secure and maintain patient privacy but we also need to use that data to make better decisions about patient care. Just like any research project needs carefully managed (and accurate) data, so too will AI-based systems. At the moment, this sees a huge competitive advantage accrue to companies like Google, that can afford to buy that data indirectly by acquiring smaller companies. But even that isn’t sustainable because there’s “no single place where all health data exists”.
This decision by the Ontario government seems to be a direct move against the current paradigm. By making patient data available to via an API, researchers will be able to access only the data approved for specific uses by patients, and it can remain anonymous. They get the benefit of access to enormous caches of health-related information while patient privacy is simultaneously protected. Of course, there are challenges that will need to be addressed including issues around security, governance, differing levels of access permissions.
And that’s just the technical issues (a big problem since medical software is often poorly designed). That doesn’t take into account the ethics of making decisions about individual patients based on aggregate data. For example, if an algorithm suggests that other patients who look like Bob tend not to follow medical advice and default on treatment, should medical insurers deny Bob coverage? These and many other issues will need to be resolved before AI in healthcare can really take off.
His most dispiriting observations are those about what social media does to politics – biased, “not towards the left or right, but downwards”. If triggering emotions is the highest prize, and negative emotions are easier to trigger, how could social media not make you sad? If your consumption of content is tailored by near limitless observations harvested about people like you, how could your universe not collapse into the partial depiction of reality that people like you also enjoy? How could empathy and respect for difference thrive in this environment? Where’s the incentive to stamp out fake accounts, fake news, paid troll armies, dyspeptic bots?
I’ve just started reading this (very short) book and it’s already making me weigh up the reasons for keeping my Twitter account. The major benefit I get is that, every so often, my feed will surface a person I’m not familiar with, who writes (or shares information) about a topic I’m interested in. However, I’m also aware that there are other places I could go to more intentionally find out who those people are, and follow them in a different way. For example, most of the time they’re writing on their own blogs, or on Medium. But that’s not where they share the links to the pieces that they care about. I worry that, by deleting my Twitter account, I would lose the serendipitous connections that it facilitates. Maybe a good place to start is by turning off the notifications for @mentions.
Note: I deleted my Facebook account about 2 years ago, I don’t spend much time on Google+, I don’t use LinkedIn or ResearchGate as social media, and I never got into Instagram or Snapchat, so Twitter is the one account I’m still active on.
There are any number of reasons why people don’t get medical care or don’t follow up on a referral to a specialist. They might not think they have a serious problem. They might lack time off work, reliable transportation, or health insurance. And those are problems AI alone can’t solve.
There’s a good point to be made here; an algorithm may be 100% accurate in diagnosing a condition but the system can still fail for many reasons, one of which may be the all too human characteristic of ignoring medical advice. Of course, there are many good reasons for why we may not be able to follow the advice, which is mentioned in the article. However, the point is that, even if an algorithm gets it absolutely right, it may still not be the solution to the problem.
Note: I mentioned this story a few posts ago. It’s going to be interesting to follow it and see how the system fares in the uncertainty of real-world situations.
“If we don’t change our overall economic and policy framework, what we’re going towards is greater wage inequality, greater income and wealth inequality and probably more unemployment and a more divided society. But none of this is inevitable,” he says. “By changing the rules, we could wind up with a richer society, with the fruits more equally divided, and quite possibly where people have a shorter working week. We’ve gone from a 60-hour working week to a 45-hour week and we could go to 30 or 25.”
A good article on the benefits and potential pitfalls of increased automation as a result of AI across a range of industries, with plenty of links to more reading. Stiglitz provides plenty of room for optimism but at the same time urges that we make intentional choices about how these things play out. He specifically advises us (i.e. society) not to leave decision-making and regulation up to the private companies, who cannot be trusted (and have no incentives) to police themselves.
However, he does not seem to say unequivocally that we are “moving towards a more divided society”, and seems to spend more time focusing on issues of privacy and data protection for consumers in the face of corporate monopolisation. It’s a pity that an otherwise well-balanced piece is marred with the click-bait title.
While it’s hard to anticipate all the future benefits of genomic data, we can already see one unavoidable challenge: the nearly inconceivable amount of digital storage involved. At present the cost of storing genomic data is still just a small part of a lab’s overall budget. But that cost is growing dramatically, far outpacing the decline in the price of storage hardware. Within the next five years, the cost of storing the genomes of billions of humans, animals, plants, and microorganisms will easily hit billions of dollars per year. And this data will need to be retained for decades, if not longer.
Interesting article that gets into the technical details of compression technologies as a way of avoiding the storage problem that comes with the increasing digitalisation of healthcare information.
Associated with this (although not covered in this article) is the idea that we’re moving from a system in which data gathering and storage is emphasised (see any number of articles on the rise of Big Data), towards a system in which data analysis must be considered. Now that we have (or soon will have) all this data, what are we going to do with it? Unless we figure out how to use to improve healthcare then it’s pretty useless.