TURN IT OFF
A future in which human workers are replaced by machines is about to become a reality at an insurance firm in Japan, where more than 30 employees are being laid off and replaced with an artificial intelligence system that can calculate payouts to policyholders.
Fukoku Mutual Life Insurance believes it will increase productivity by 30% and see a return on its investment in less than two years. The firm said it would save about 140m yen (£1m) a year after the 200m yen (£1.4m) AI system is installed this month. Maintaining it will cost about 15m yen (£100k) a year.
The move is unlikely to be welcomed, however, by 34 employees who will be made redundant by the end of March.
The system is based on IBM’s Watson Explorer, which, according to the tech firm, possesses “cognitive technology that can think like a human”, enabling it to “analyse and interpret all of your data, including unstructured text, images, audio and video”.
The technology will be able to read tens of thousands of medical certificates and factor in the length of hospital stays, medical histories and any surgical procedures before calculating payouts, according to the Mainichi Shimbun.
Since April 2016, Facebook has been automatically adding
alttags to images you upload that are populated with keywords representing the content of your images:<img csrc=“https://facebook.com/some-url.jpg”
alt=“Image may contain: golf, grass, outdoor and nature” width=“316” height=“237”>They are labeling your images using a Deep ConvNet built by Facebook’s FAIR team.
On one hand, this is really great. It improves accessibility for blind users who depend on screen readers which are only capable of processing text.
But I think a lot of internet users don’t realize the amount of information that is now routinely extracted from photographs. Facebook (and Google, Apple, Amazon, etc) can easily tell from your photographs if you have a pet dog, if you collect cameras, if you play golf, if you have children, or if you are just really into sunglasses.
Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records
Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.
Superintelligence - The Idea That Eats Smart People
As I mentioned earlier, the most effective way we’ve found to get interesting behavior out of the AIs we actually build is by pouring data into them.
This creates a dynamic that is socially harmful. We’re on the point of introducing Orwellian microphones into everybody’s house. All that data is going to be centralized and used to train neural networks that will then become better at listening to what we want to do.
But if you think that the road to AI goes down this pathway, you want to maximize the amount of data being collected, and in as raw a form as possible.
It reinforces the idea that we have to retain as much data, and conduct as much surveillance as possible.
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The pressing ethical questions in machine learning are not about machines becoming self-aware and taking over the world, but about how people can exploit other people, or through carelessness introduce immoral behavior into automated systems.
And of course there’s the question of how AI and machine learning affect power relationships. We’ve watched surveillance become a de facto part of our lives, in an unexpected way. We never thought it would look quite like this.
video:
Google’s decision to reorganize itself around A.I. was the first major manifestation of what has become an industrywide machine-learning delirium. Over the past four years, six companies in particular — Google, Facebook, Apple, Amazon, Microsoft and the Chinese firm Baidu — have touched off an arms race for A.I. talent, particularly within universities. Corporate promises of resources and freedom have thinned out top academic departments.
The major advancements in Deep Learning in 2016
Deep Learning has been the core topic in the Machine Learning community the last couple of years and 2016 was not the exception. In this article, we will go through the advancements we think have contributed the most (or have the potential) to move the field forward and how organizations and the community are making sure that these powerful technologies are going to be used in a way that is beneficial for all.
If LawBot Wants To Know About My Anus I’m Sure It Has A Reason
Over at Associate’s Mind they had a chat with a hot new LawBot designed to automate dispensing of legal advice. I decided to give it a try in the guise of a typical client: someone complaining that their drug dealer ripped them off.
The Administration’s Report on the Future of Artificial Intelligence
Over the past several years, in particular, issues concerning the development of so-called “Lethal Autonomous Weapon Systems” (LAWS) have been raised by technical experts, ethicists, and others in the international community. The United States has actively participated in the ongoing international discussion on LAWS in the context of the Convention on Certain Conventional Weapons (CCW), and anticipates continued robust international discussion of these potential weapon systems going forward.State Parties to the CCW are discussing technical, legal, military, ethical, and other issues involved with emerging technologies, although it is clear that there is no common understanding of LAWS.Some States have conflated LAWS with remotely piloted aircraft (military “drones”), a position which the United States opposes, as remotely-piloted craft are, by definition, directly controlled by humans just as manned aircraft are. Other States have focused on artificial intelligence, robot armies, or whether “meaningful human control” –an undefined term –is exercised over life-and-death decisions.
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The U.S. government is also conducting a comprehensive review of the implications of autonomy in defense systems.In November 2012, the Department of Defense (DoD) issued DoD Directive 3000.09,“Autonomy in Weapon Systems,” which outlines requirements for the development and fielding of autonomous and semi-autonomous weapons.Weapon systems capable of autonomously selecting and engaging targets with lethal force require senior-level DoD reviews and approval before those weapon systems enter formal development and again before fielding.The DoD Directive neither prohibits nor encourages such development, but requires it to proceed carefully and only after review and approval by senior defense officials.
AI Is Not out to Get Us
AI is doing some impressive things—last week, for example, makers of the AlphaGo computer program reported that their software has learned to navigate the intricate London subway system like a native. Even the White House has jumped on the bandwagon, releasing a report days ago to help prepare the U.S. for a future when machines can think like humans.
But AI has a long way to go before people can or should worry about turning the world over to machines, says Oren Etzioni, a computer scientist who has spent the past few decades studying and trying to solve fundamental problems in AI. Etzioni is currently the chief executive officer of the Allen Institute for Artificial Intelligence (AI2), an organization that Microsoft co-founder Paul Allen formed in 2014 to focus on AI’s potential benefits—and to counter messages perpetuated by Hollywood and even other researchers that AI could menace the human race.
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