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What about the common objection that without ‘intellectual property’ the proverbial starving artist would be at the mercy of giant corporations, who have existing market share and first-mover advantage? It is important to disaggregate the necessity and desirability of the protections of the various legal regimes of copyright, patents, trademarks and trade secrets from that of the language of ‘intellectual property’. Current copyright, patent, trade-secret and trademark law do not need to be completely rejected. Their aims are rather more modest: the reconfiguration of legal rules and protections in an economy and culture in which the nature of creative goods and how they are made, used, shared, modified and distributed has changed. Such advocacy is not against, for instance, copyright protections. Indeed, in the domain of free and open-source software, it is copyright law – through the use of artfully configured software licences that do not restrain users in the way that traditional proprietary software licences do – that protects developers and users. And neither do copyright reformers argue that plagiarists be somehow rewarded; they do not advocate that anyone should be able to take a copyrighted work, put their name on it, and sell it.

This public domain is ours to draw upon for future use. The granting of temporary leases to various landlords to extract monopoly rent should be recognised for what it is: a limited privilege for our benefit. The use of ‘intellectual property’ is a rhetorical move by one partner in this conversation, the one owning the supposed ‘property right’. There is no need for us to play along, to confuse one kind of property with another or, for that matter, to even consider the latter kind of object any kind of property at all. Doing so will not dismantle the elaborate structures of rules we have built in order to incentivise artistic and scientific work. Rather, it will make it possible for that work to continue.

If China, Russia and the U.S. are in a race for AI supremacy, the kind of manifestations of AI will be so significant, they could alter the entire future of human civilization.

THE EXPONENTIAL THREATFrom drones, to nanobots to 3D-printing, automation could lead to unparalleled changes to how we live and work. In spite of the increase in global GDP, most people’s quality of living is not likely to see the benefit as it will increasingly be funneled into the pockets of the 1%. Capitalism then, favors the development of an AI that’s fundamentally exploitative to the common global citizen.Just as we exchanged our personal data for convenience and the illusion of social connection online, we will barter convenience for a world a global police state where social credit systems and AI decide how much of a “human stipend” (basic income) we receive. Our poverty or the social privilege we are born into, may have a more obscure relationship to a global system where AI monitors every aspect of our lives.Eventually AI will itself be the CEOs, inventors, master engineers and creator of more efficient robots. That’s when we will know that AI has indeed replaced human beings. What will Google’s DeepMind be able to do with the full use of next-gen quantum computing and supercomputers?

Artificial Intelligence Will Replace HumansTo argue that AI and robots and 3D-printing and any other significant technology won’t impact and replace many human jobs, is incredibly irresponsible.That’s not to say humans won’t adapt, and even thrive in more creative, social and meaningful work!That AI replacing repetitive tasks is a good thing, can hardly be denied. But will it benefit all globally citizens equally? Will ethics, common sense and collective pragmatism and social inclusion prevail over profiteers?Will younger value systems such as decentralization and sustainable living thrive with the advances of artificial intelligence?Will human beings be able to find sufficient meaning in a life where many of them won’t have a designated occupation to fill their time?These are the question that futurists like me ponder, and you should too.

The power of the system is its “ability to recognize that correlations occur between gender and professions,” says Kathryn Hume. “The downside is that there’s no intentionality behind the system—it’s just math picking up on correlations. It doesn’t know this is a sensitive issue.” There’s a tension between the futuristic and the archaic at play in this technology. AI is evolving much more rapidly than the data it has to work with, so it’s destined not just to reflect and replicate biases but also to prolong and reinforce them.

And sometimes, even when ample data exists, those who build the training sets don’t take deliberate measures to ensure its diversity

But not everyone will be equally represented in that data.

Accordingly, groups that have been the target of systemic discrimination by institutions that include police forces and courts don’t fare any better when judgment is handed over to a machine.

A growing field of research, in fact, now looks to apply algorithmic solutions to the problems of algorithmic bias.

Still, algorithmic interventions only do so much; addressing bias also demands diversity in the programmers who are training machines in the first place.

A growing awareness of algorithmic bias isn’t only a chance to intervene in our approaches to building AI systems. It’s an opportunity to interrogate why the data we’ve created looks like this and what prejudices continue to shape a society that allows these patterns in the data to emerge.

Of course, there’s another solution, elegant in its simplicity and fundamentally fair: get better data.