Why the Terminator is not helping us talk about the future of AI.

greyish cement cube floating 10 feet in the air, in a well lit, white room.
greyish cement cube floating 10 feet in the air, in a well lit, white room.
Photo by Christian Fregnan on Unsplash

With the ever growing advances in the field of AI, many public and private discussions on the subject are underlined by the archetype of an agent, or “cyborg” if anthropomorphised, autonomously making decisions for itself in the world. The image that comes to mind often is that of the Terminator, James Cameron’s 1984 movie. This is a very fertile ground for discussion as it threatens, consciously or not, the very understanding of who we are as humans. And the terminator was devilishly close to a human, able to communicate its intentions and therefore possessing a copious amount self-awareness. Today I’d like to make the case that though most definitions of autonomy revolve around the “self”, involving self-determination or self-law making for example, an intrinsic definition of autonomy may not exist. …

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Photo by You X Ventures on Unsplash

As much as the prowess of AI are lauded in the media and its impact in many industries create value for some, the penetration of this technology into the foundations of our institutions will not happen unless the systems they are incorporated into respond to some basic precepts of human centered design. This is why the field of Explainable AI, or XAI - a term coined by the DARPA (Defense Advanced Research Project Agency)- is a burgeoning field. In their own words:

“ The effectiveness of AI systems will be limited by the machine’s inability to explain its thoughts and actions to human users. Explainable AI will be essential, if users are to understand, trust and effectively manage this emerging generation of human partners.” …

The hidden flaws of the standard

The fact that ERC20 is nowadays the most widely used standard for smart contracts is a blessing in disguise. It allows the economy of tokens on the Ethereum blockchain to run fairly smoothly and enables many cross-contract behavior. But in most of the analyses that I have seen, there is a definite overconfidence in the standard.

If you read the definition of the ERC20 standard you will find something well written and quite straight forward. But you will most likely also overlook one small detail that can make the world of difference in analyzing some contracts. It is in the relationship between the transfer() and transferFrom() methods and the Transfer() event. …

Data science is fun, the more I do it, the more I see how exciting forensics could be. Lately I have been diving in the Bitcoin ledger, trying to see what’s to be found there. It is an extremely rich dataset and very little work has been done still, everyone being more interested in the off-chain data of trading histories. But I’ll show you slowly that there is a lot of things that are worth looking at in the data.

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“A person reading a book with a magnifying glass and a pen in hand” by João Silas on Unsplash

Let us start with a very strange phenomenon. It is linked to the fact that bitcoin transactions are, contrary to usual banking transactions, not necessarily one-to-one. …

A mantra that we’ve all heard about blockchain technology and bitcoin in particular is that “The data is public, the information is accessible to all”. But how accessible is it really? The answer is not that accessible.

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Photo by Chris Liverani on Unsplash

There are tools people use to get information on what is happening in the bitcoin blockchain. One of the most used is blockchain.com, which is a private company with a misleading name (so much so that the company’s LinkedIn employee’s list is infatuated with people that aren’t in any shape or form connected to the company). But for an analyst or economist, these tools only give snippets of activity and don’t allow to extract meaningful knowledge on the state of the bitcoin economy. …

A step-by-step guide to running your models on EC2 instances.

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“Numbers on the starting line on a red running track” by Austris Augusts on Unsplash

You heard the Gospel. This is the decade of Artificial Intelligence. You picked up Machine Learning (this is how we insiders say, right?) as a hobby, you are prepping your next career move or you’re a young scientist seriously thinking about leaving academia to go be successful in the new economy. You’ve heard many times about Kaggle, the de-facto king of ML competition platforms.

So you signed up to a competition all pumped up, aiming for a top tier position. A bronze medal at least. You’ve gone through all the online courses. You’re ready for the big leagues. You know in your heart that your cleverness and skills will prevail, that you will find that magic feature, or get that angle on the problem that nobody got. After all you are different. …


Dany Majard

A no-nonesense take on data science and blockchain.

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