Hello World
Hello World: How to be Human in the Age of Machine by Hannah Fry
🚀 The Book in 3 Sentences
We generally think of algorithms as being life-changing or catastrophic. As being something we don't want to use at all or only use. But we can find better solutions with a mixture of algorithms and humans.
There is a tendency to abandon algorithms when we don't like the outcome. However, when we evolve, update and adjust the algorithm and AI we get more desired results.
In the age of the algorithm, humans have never been more important. We need to think about our interaction with data, machines, and algorithms, and this is up to us.
🎨 Impressions
This book presented an an alternative view to other algorithm/data books. There was a focus on the human element and our interaction with the data.
The tone and writing style was open-minded throughout. There were interesting cases and stories, very applicable to everyday life. Big questions were raised about how we want our future society to look with the integration of technology.
How I Discovered It
Hannah Fry's work includes studying the patterns of human behaviour, such as interpersonal relationships and dating and how mathematics can apply to them. These ideas in combination with the data was an interesting read, as I wanted to learn more about how data would influence our future.
Who Should Read It?
If you are interested in how algorithms impact us or how they could or don't believe the impact algorithms could have on changing the future then this is a must read. This book takes a more optimistic view on how data will impact the world.
✍️ My Top 3 Quotes
We should take algorithms off the pedestal, examine them a bit more carefully and ask if they’re really capable of doing what they claim
The data and algorithm can’t feel the break up and lonely Wednesday morning that humans can.
We should stop viewing the machines as objective masters and start treating them as we would any other source of power. By questioning their decisions, scrutinizing their motives, acknowledging our emotions, demanding to know who stands to benefit, holding them accountable for their mistakes, and refusing to comply and be complacent.
📒 Summary + Notes
Four main categories of algorithms:
Prioritisation: making an ordered list: Google search, Netflix, TomTom fastest route
Classification: picking a category. Marriage or YT classify you based on interests/characteristics
Association: finding links. Dating; Amazon's recommendations
Filtering: isolating what's important. Siri, Alexa need to filter our voice from background. Personalised Twitter or Facebook feed
Power:
This tendency of Lost a few things in black-and-white - seeing algorithms is either the omnipotent masters or useless pile of junk - that presents quite a problem in a high tech age. If we’re going to get the most out of technology, we’re going to need to work out a way to make it a bit more objective. We need to acknowledge our own flaws, question our gut reactions and be a bit more aware of our feelings towards the algorithms around us. On the flipside, we should take algorithms off the pedestal, examine them a bit more carefully and ask if they’re really capable of doing what they claim. That’s the only way to decide if they deserve the power they’ve been given.
Unfortunately, all this is often much easier said than done. Often times, we have a little say over the power in breach of the algorithms that surround us, even when it comes to those that affect us directly. This is particularly true for the algorithms that trade in the most fundamental modern commodity: data. The algorithms that silently follow us around the Internet, the ones that are harvesting our personal information, invading our privacy and interfering with our character to subtly influence our own behaviour. In a perfect storm of this trust, power and influence placed on them, the consequences have the potential to fundamentally alter our society.
Data:
We often have a false sense of privacy as there are many hidden incentives at play.
We often find ourselves asking: Would I be better off without an algorithm?
We should not forger the role data can play in our lives. Data and algorithms don't just have the power to predict shopping habits. They also have the power to rob someone of their freedom.
Justice:
Often algorithms and their related intellectual property is protected as a 'trade secret.' But surely, there is a moral duty to come clean about pitfalls and flaws
There is inequality in algorithms - based on past events. This is the case even if it is not a specific factor eg race/gender. Because in the past, more men have committed homicide, the algorithm is going to take this into account. Therefore, the data we give has inherent algorithm, not the algorithm themselves.
Medicine:
Everyone in the healthcare system is working towards the same goal - getting the patient better. But even here every party in the process, everyone has subtly different set of a objectives. But even here every party in the process has certainly different set of a jet objectives. If an algorithm is introduced, there will always be some kind of balance: Between privacy and public good; Between individuals in a population; Between different challenges and priorities.
It isn’t easy to find a path through the tangle of incentives, even when there is a clear prize of better healthcare for all at the end. But it’s even harder when the completing incentives are hidden from view. When the benefits of an algorithm are overstated and the risks are scarce.
Cars:
Using AI in cars poses a problem with images as technology struggles to understand distance and perspective. Furthermore, how do you describe a road - as that description could also be used for the an image of a tree.
Where are you? What's around you? What should you do? These questions becomes challenging to answer when feeding information into an algorithm.
A possible solution is Bayes Theorem. This is based on the idea of building up data and evidence and makes a best guess from the information available. It essentially measures your belief in something.
Crime:
Facial recognition could provide significant advances in this area. But how do we take into account mis-identification issues. How much reliance do we place on things like this and how do we deal with the consequences of getting it wrong.
Art/Music:
IMBD have collected lots of data which could be used to create new business opportunities through the use of algorithms. They never envisaged that this data would be worth lots of money now. But, an algorithm that could decide which movie should be made, how much it could generate and which actors it should use would be a significant development. This is the potential of an algorithm.
Can music ever be original if it is computer generated. The data and algorithm can’t feel the break up and lonely Wednesday morning that humans can.
Conclusion:
Imagine that, rather than exclusively focusing our attention on designing algorithms to adhere to some impossible standard of perfect fairness, we instead designed them to facilitate redress when they inevitably erred; that we put as much time and effort into ensuring that automatic systems was easy to challenge as they are to implement. Perhaps the answer is to build our algorithms to be constable is from the ground up. Imagine that we designed them to support humans in their decisions, rather than instruct them. To be transparent about why they came to a particular decision, rather than just inform us of the results.
In my view, the best algorithms are the ones that take the human into account at every stage. The ones that recognize our habits instead of over trusting the output of the machine, while embracing their own flaws and wearing that uncertainty proudly front and centre.
The algorithm and human working together in partnership, exploiting each other’s strengths and embracing each other‘s flaws is the answer we should be aiming for.
We should stop viewing the machines as objective masters and start treating them as we would any other source of power. By questioning their decisions, scrutinizing their motives, acknowledging our emotions, demanding to know who stands to benefit, holding them accountable for their mistakes, and refusing to comply and be complacent. I think this is the key to the future where the net overall affect of algorithms is a positive force for society. And it’s only right that this job rests squarely on our shoulders. Because one thing is for sure. In the age of the algorithm, humans have never been more important.
