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In industry, there is blunt-force algorithmic tension – ‘Efficiency, capitalism, commerce!’ versus ‘Robots are stealing our jobs!’ But for algorithmic art, the tension is subtler. Only 4 per cent of the work done in the United States economy requires ‘creativity at a median human level’, according to the consulting firm McKinsey and Company. So for computer art – which tries explicitly to zoom into this small piece of that vocational pie – it’s a question not of efficiency or equity, but of trust. Art requires emotional and phrenic investments, with the promised return of a shared slice of the human experience. When we view computer art, the pestering, creepy worry is: who’s on the other end of the line? Is it human? We might, then, worry that it’s not art at all.

But the honest-to-God truth, at the end of all of this, is that this whole notion is in some way a put-on: a distinction without a difference. ‘Computer art’ doesn’t really exist in an any more provocative sense than ‘paint art’ or ‘piano art’ does. The algorithmic software was written by a human, after all, using theories thought up by a human, using a computer built by a human, using specs written by a human, using materials gathered by a human, at a company staffed by humans, using tools built by a human, and so on. Computer art is human art – a subset rather than a distinction. It’s safe to release the tension.

JUST ADD WATERGrowing your own veggies may become possible even for urbanites with tiny studio apartments.Israeli startup Living Box offers a modular, unfoldable, solar-powered little greenhouse that you can use to harvest anything from tomatoes to tea and herbs.“We have a slow release water system for irrigation, with a novel liquid nutrient solution and bacteria to avoid the use of pesticides, as well as an app prototype updating weather conditions and other relevant data right to your smartphone, so you don’t have to monitor it,” explained Nitzan Solan, CEO of the company.The idea was to create a sustainable, affordable and simple mobile farming system that could be operated by anyone around the globe.As of now, Living Box is testing in 50 sites around Israel, the U.S. and Nigeria, and aims to try locations in Spain and Fiji. It is expected to carry a market price of $300.

Struggling to fill jobs in the Charlotte plant, Siemens in 2011 created an apprenticeship program for seniors at local high schools that combines four years of on-the-job training with an associate degree in mechatronics from nearby Central Piedmont Community College. When they finish, graduates have no student loans and earn more than $50,000 a year.

AI tools will help creative people be more creative and strategic people be more strategic, so core people can actually be more human, Lee said. "Jobs like doctors will require more EQ [emotional intelligence], more compassion, more human-to-human interaction, while AI takes over more the analytical, diagnostic work."

"We see AI changing 90 percent of the work people do," Daugherty said. "Fifteen percent of jobs will be completely automated and replaced. But the major of jobs will be improved."

"There is a lot of a counterweight of investors who really care about this stuff," said Paula Goldman, leader of the Tech and Society Solutions Lab at the Omidyar Network, citing the potential for indices that track how well companies follow best practices. "You can reframe [AI response] as a business risk."

The term artificial intelligence typically refers to automation of tasks by software that previously required human levels of intelligence to perform. While machine learning is sometimes used interchangeably with AI, machine learning is just one sub-category of artificial intelligence whereby a device learns from its access to a stream of data.When we talk about AI spending, we’re typically talking about investment that companies are making in building AI capabilities. While this may change in the future, McKinsey estimates that the vast majority of spending is done internally or as an investment, and very little of it is done purchasing artificial intelligence applications from other businesses.

62% of AI spending in 2016 was for machine learning, twice as much as the second largest category computer vision. It’s worth noting that these categories are all types of “narrow” (or “weak”) forms of AI that use data to learn about and accomplish a specific narrowly defined task. Excluded from this report is “general” (or “strong”) artificial intelligence which is more akin to trying to create a thinking human brain.

The McKinsey survey mostly fits well as evidence supporting Cross’s framework that large profitable industries are the most fertile grounds of AI adoption. Not surprisingly, Technology is the industry with highest AI adoption and financial services also makes the top three as Cross would predict.Notably, automotive and assembly is the industry with the second highest rate of AI adoption in the McKinsey survey. This may be somewhat surprising as automotive isn’t necessarily an industry with the reputation for high margins. However, the use cases of AI for developing self-driving cars and cost savings using machine learning to improve manufacturing and procurement efficiencies are two potential drivers of this industry’s adoption.

With such ubiquity comes power and influence. And along with the technology’s benefits come worries over privacy and personal freedom. Yes, AI can take some of the time and effort out of decision-making. But if you are a woman, a person of color, or a member of some other unlucky marginalized group, it has the ability to codify and worsen the inequalities you already face. This darker side of AI has led policymakers such as U.S. Senator Kamala Harris to advocate for more careful consideration of the technology’s risks.

Today, we can see a similar pattern emerging with the deployment of AI. Good intentions are nice, but they must account for and accommodate a true diversity of perspectives. How can we trust the ethics panels of AI companies to take adequate care of the needs of people of color, queer people, and other marginalized communities if we don’t even know who is making the decisions? It’s simple: we can’t and we shouldn’t.

Megatrends such as digitization, the rise of automation, and shifting demographics are disrupting the way we work, and the way companies relate to workers.

We at PwC have spent some time envisioning four alternative future worlds of work, each named with a color. These admittedly extreme examples of how work could look in 2030 are shaped by the ways people and organizations respond to the forces of collectivism and individualism, on one axis, and integration and fragmentation on the other. These scenarios can help organizations think through possibilities and how they will prepare to meet them. One prospect is that the world could move away from big company capitalism as technology enables small businesses and niche marketers to become more powerful. Or collectivism could take priority, as societies and companies work together through a sense of shared responsibility. Will “me first” prevail, or will societies come together for the greater good? Will digital technology mark the end for large companies, or will it enable large companies to slash their internal and external costs and become more powerful?

The whole premise of a true universal income program is that people can be eligible to receive the supplemental payment regardless of whether or not they work. While the income threshold for receiving the benefit necessarily varies by context, generally the idea is to help people clear the poverty threshold wherever they live.

In contrast, a significant part of the appeal of true UBI programs now comes from the fact that jobs–especially for less highly skilled workers–have not proven to be an adequate source of economic stability. And with automation threatening to make low-skilled jobs like cashier or waiter redundant, we can’t continue to rely on jobs as a path out of poverty. A UBI could certainly enable people to work if they would like to, but the point of it is that economic well-being should be a guarantee for all, regardless of labor.

Since 1982, the Alaskan government has sent a check to every resident from an investment fund established with oil revenues. In 2015, when oil prices were high, the Alaska Dividend Fund sent every individual an annual check for $2,072, or $8,288 for a family of four. Nobody is arguing that this annual stipend is enough to live on, or to qualify as a true UBI (especially because it fluctuates with the price of oil), but it’s a proven model for how a government can collect and redistribute revenue. Perhaps, Marinescu says, Alaska could be instructive in how states could put a tax on carbon and use the revenues to boost incomes.

Those who seek a radical departure from capitalism see UBI as part of a radical platform to move away from a world in which work is central to our lives, identities and economies. In their book Inventing the Future, Alex Williams and Nick Srnicek argue that UBI is a fundamental part of delivering a new economy in which citizens have much greater freedom over when and if they work.

What this shows is that for UBI to be a viable proposition at these levels, there would need to be a fundamental transformation in the ownership of the economy. Williams and Srnicek acknowledge this, arguing that UBI will only work in combination with large scale and collectively owned automation, a reduction in the working week and a shift in social attitudes around the value of the ‘work ethic’.

Action on relative poverty is important, and inequality is not cost free. As Kate Pickett and Richard Wilkinson show in their book ‘The Spirit Level’, countries with higher rates of inequality perform worse against a range of social outcomes – physical health, mental health, drug abuse, education, imprisonment, obesity, social mobility, trust and community life.

Crispr, the powerful gene-editing tool, is revolutionizing the speed and scope with which scientists can modify the DNA of organisms, including human cells. So many people want to use it—from academic researchers to agtech companies to biopharma firms—that new companies are popping up to staunch the demand. Companies like Synthego, which is using a combination of software engineering and hardware automation to become the Amazon of genome engineering. And Inscripta, which wants to be the Apple. And Twist Bioscience, which could be the Intel

“Being able to do that in a parallel way is the novel part,” says Paul Dabrowski, who estimates that Synthego cuts down the time it takes for a scientists to perform gene edits from several months to just one.

They’re betting biology will be the next great computing platform, DNA will be the code that runs it, and Crispr will be the programming language.