Our proposal has three components: The first is a public data pool that would make data accessible to registered users. Local, state and federal governments have sizable data resources that would seed this digital commons. Users would be verified to block foreign governments, hackers and others with ill motives from access, and users would be prevented from using the data to engage in racial or other forms of discrimination and for microtargeted advertising. Some of the data may be very sensitive, and access to those resources would be highly regulated. We can imagine a variety of ways that regulation and technology together could protect privacy and still foster innovation: Data could be anonymized at the source; the commons could have an interface that allowed users to derive insight from the data set, while leaving the underlying information inaccessible; less sensitive data, like weather information, could be made available in a format optimized for training A.I. What's more, methods for safely sharing A.I. models without disclosing the underlying data are being developed today and could enable users of the data commons to collaborate on public-interest A.I. services.

AI (Artificial Intelligence) is everywhere and it's here to stay.

AI (Artificial Intelligence) is everywhere and it's here to stay. Along with these consumer applications, companies across sectors are increasingly harnessing AI's power for productivity growth and innovation. There are many who believe that AI has the potential to become more significant than even the internet. Availability of enormous amount of data combined with huge leap in computational power and huge improvements in engineering skills should help AI, backed with deep learning, to make huge impact across various facets of human life. Amid all the hype, genuine and inflated, around the world of AI, it is pertinent to ask an important question.

There are a lot of questions that come with the possibilities of artificial intelligence. If AI machines can behave like humans, they can begin to replace their jobs as well. Unless regulations were put into place to prevent AIs from taking jobs, there would be few big businesses willing to hire human workers for specific work. There are examples of work that has already been replaced by robots and as long as technology grows, artificial intelligence will continue to as well. Fortunately, many scientists and engineers believe AI will never be able to replicate human judgement.

Humans have a tendency to make things smarter and smarter: the telephone became a smartphone, the wristwatch became a smartwatch. Another example is where humans enabled a computer to ingest data, process it, provide an outcome, then learn from additional new data and provide an improved outcome. In layman terms, this is cognition and technologies that enable cognition are cognitive technologies such as Machine Learning, Natural Language Processing, Natural Language Generation, etc. This in my view, is one of the most important change that will impact the human race. I believe this will compliment humans and not replace them.

You've probably heard that we're in the midst of an A.I. revolution. We're told that machine intelligence is progressing at an astounding rate, powered by "deep learning" algorithms that use huge amounts of data to train complicated programs known as "neural networks." Today's A.I. programs can recognize faces and transcribe spoken sentences. We have programs that can spot subtle financial fraud, find relevant web pages in response to ambiguous queries, map the best driving route to almost any destination, beat human grandmasters at chess and Go, and translate between hundreds of languages. What's more, we've been promised that self-driving cars, automated cancer diagnoses, housecleaning robots and even automated scientific discovery are on the verge of becoming mainstream.

You've probably heard that we're in the midst of an A.I. revolution. We're told that machine intelligence is progressing at an astounding rate, powered by "deep learning" algorithms that use huge amounts of data to train complicated programs knows as "neural networks." Today's A.I. programs can recognize faces and transcribe spoken sentences. We have programs that can spot subtle financial fraud, find relevant web pages in response to ambiguous queries, map the best driving route to almost any destination, beat human grandmasters at chess and Go, and translate between hundreds of languages. What's more, we've been promised that self-driving cars, automated cancer diagnoses, housecleaning robots and even automated scientific discovery are on the verge of becoming mainstream.

The idea of shifting decision-making to algorithms and artificial intelligence is a compelling one: It can, so the theory goes, remove the inconsistencies and prejudices of human decision-making, and make a fairer world. Life-changing decisions are inevitably affected by our human biases and frailties. One Israeli study, for instance, found that, at the beginning of the day, judges granted parole in two-thirds of cases, granted close to zero just before the lunch break and then granted more again after lunch, once the judges had eaten and taken a break. So if we can't even tackle bad decision-making because we're hungry, how do we even begin to tackle deeper-seated biases? Shifting away from a world so affected by human fallibility therefore seems compelling, but it comes with huge dangers that could entrench existing divides and failures.

This is the weekend roundup of The WorldPost, of which Nathan Gardels is the editor in chief. Technology is not a neutral tool. Its use is infused with the cultural ethos of those who deploy it. Digital connectivity may have been conceived in the algorithmic imagination of Silicon Valley libertarians as a way to free the individual from institutions. But in the hands of China's institutional civilization, shaped for millennia by a communitarian and authoritarian mindset, it further empowers the state.

A recent MIT Technology Review article titled "The Dark Secret at the Heart of AI" warned: "No one really knows how the most advanced algorithms do what they do. That could be a problem." Thanks to this uncertainty and lack of accountability, a report by the AI Now Institute recommended that public agencies responsible for criminal justice, health care, welfare and education shouldn't use such technology. Given these types of concerns, the unseeable space between where data goes in and answers come out is often referred to as a "black box" -- seemingly a reference to the hardy (and in fact orange, not black) data recorders mandated on aircraft and often examined after accidents. In the context of A.I., the term more broadly suggests an image of being in the "dark" about how the technology works: We put in and provide the data and models and architectures, and then computers provide us answers while continuing to learn on their own, in a way that's seemingly impossible -- and certainly too complicated -- for us to understand.