This rather ostentatious remark made by Marvin Minsky, co-founder of the world-famous MIT Artificial Intelligence Laboratory, was referring to the fact that researchers have been primarily concerned with small facets of machine intelligence as opposed to looking at the problem as a whole. This article examines the contemporary issues of artificial intelligence (AI) and looks at the current status of the AI field together with potent arguments provided by leading experts to illustrate whether AI is an impossible concept to obtain. Because of the scope and ambition, artificial intelligence defies simple definition. Initially, AI was defined as "the science of making machines do things that would require intelligence if done by men." This somewhat meaningless definition shows how AI is still a young discipline -- and similar early definitions have been shaped by the technological and theoretical progress made on the subject.

Numerai is a hedge fund that's using technology to create an unprecedented network effect, and transform the way money is managed. Crowdsourced investment strategies are many and varied, but Numerai crowdsources machine intelligence in a totally unique way by supplying its network of data scientists with encrypted data on which to test their machine learning models, thus removing any bias attached to the application of the algorithms. These models are entered into a monthly tournament and the best ones receive a pay-out. This was previously done using Bitcoin (because it was efficient and more anonymous than PayPal), but more recently Numerai launched its own token, Numeraire (NMR), on Ethereum, the public blockchain which has spawned a multitude of trustless, decentralized applications. The aim of the token was to create more value for Numerai's growing network of scientists, and further align them with the collaborative goals of the project.

So, I compiled all the grand promises that the world's traditional carmakers have made in the past two years or so, and one thing is clear: Either the automotive world is going to undergo a radical transformation around 2020, or these companies have seriously erred in their planning. Volkswagen corporate is engaged in a major initiative they've dubbed "Together-Strategy 2025," which ties together the electrification and smartening of cars. As part of that, they've promised to "bring highly automated driving functions to market as a core competency from 2021." Recently, they introduced an on-demand self-driving car-like thing, which sort of looks like a character in Thomas the Tank Engine: Future Edition. Audi, which is a part of the Volkswagen Group, has been more aggressive.

I built Pegg, an autonomous chatbot that helps people manage their money, with ethics and accountability in mind, because both are important to me in my own work life. In the process, my team at Sage and I saw a clear demand among industry peers for a set of working-level principles that every company building AI should consider. While they could complement the visionary Asilomar Principles backed by Elon Musk, Stephen Hawking and other innovation giants–which are designed to instill caution into the AI-creation process–they should be crafted specifically for businesses developing AI, and their customers, who will be the end-users of this emerging tech. Here's what I believe needs to happen to drive the ethical development of corporate AI over the next few decades, and, in the process, make humans working with the AI more accountable, as well. In building and deploying bot technology, businesses and builders need to create diverse AI.

COVER A conceptual illustration of an artificial neuron evokes a technology that is transforming many fields of science: artificial intelligence (AI). One common form of AI is a neural network, which "learns" as connections between simulated neurons change in response to inputs. Such systems can find meaningful patterns in vast data sets, ranging from genomics to astronomy, and are even beginning to design experiments.

Samsung's technological reach is formidable, shipping more phones than any other manufacturer. The company boasts nearly 23% of the global smartphone market, and its Gear VR headset, available since late 2015, is already among the most popular virtual reality devices going. But when it comes to voice-activated speakers, a medium that some believe is on the cusp of becoming the next major computing platform, there's reason to question whether Samsung has the wherewithal to keep up. The South Korean technology giant may be developing a new Amazon Echo-like smart speaker powered by its Bixby virtual assistant, reports the Wall Street Journal. But it's arrival would likely come long after category pioneers like Amazon, Google and Apple have either released or announced plans to launch voice-activated gadgets of their own.

With billions of users and hundreds of billions of tweets and posts every year, social media has brought big data to social science. It has also opened an unprecedented opportunity to use artificial intelligence (AI) to glean meaning from the mass of human communications. The University of Pennsylvania's Positive Psychology Center, for example, uses machine learning and natural language processing to sift through gobs of data to gauge the public's emotional and physical health, including levels of depression and trust, and several personality traits. That's traditionally done with surveys. But social media data is cheap and abundant. It is also messy, but AI offers a powerful way to reveal patterns.

London's Royal Free hospital failed to comply with the Data Protection Act when it handed over personal data of 1.6 million patients to DeepMind, a Google subsidiary, according to the Information Commissioner's Office. The data transfer was part of the two organisation's partnership to create the healthcare app Streams, an alert, diagnosis and detection system for acute kidney injury. The ICO's ruling was largely based on the fact that the app continued to undergo testing after patient data was transferred. Patients, it said, were not adequately informed that their data would be used as part of the test. "Our investigation found a number of shortcomings in the way patient records were shared for this trial," said Elizabeth Denham, the information commissioner.

Paul Tang was with his wife in the hospital just after her knee replacement surgery, a procedure performed on about 700,000 people in the U.S. every year. The surgeon came by, and Tang, who is himself a primary-care physician, asked when he expected her to be back at her normal routines, given his experience with patients like her. The surgeon kept giving vague non-answers. "Finally it hit me," says Tang. "He didn't know." Tang would soon learn that most physicians don't know how their patients do in the ordinary measures of life back at home and at work--the measures that most matter to patients.

The Stanford Heuristic Programming Project: Goals and Activities