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Ethics of artificial intelligence
The ethics of artificial intelligence is the part of the ethics of technology specific to robots and other artificially intelligent beings.
divided into roboethics, a concern with the moral behavior of humans as they design, construct, use and treat artificially intelligent beings, and machine ethics, which is concerned with the moral behavior of artificial moral agents (AMAs).
The term 'robot ethics' (sometimes 'roboethics') refers to the morality of how humans design, construct, use and treat robots and other artificially intelligent beings.
It has been suggested that robot rights, such as a right to exist and perform its own mission, could be linked to robot duty to serve human, by analogy with linking human rights to human duties before society.
Pamela McCorduck counters that, speaking for women and minorities 'I'd rather take my chances with an impartial computer,' pointing out that there are conditions where we would prefer to have automated judges and police that have no personal agenda at all.
However, Kaplan and Haenlein stress that AI systems are only as smart as the data used to train them since they are, in their essence, nothing more than fancy curve-fitting machines: Using AI to support a court ruling can be highly problematic if past rulings show bias toward certain groups since those biases get formalized and engrained, which makes them even more difficult to spot and fight against.
The new recommendations focus on four main areas: humans and society at large, the private sector, the public sector, and research and academia.
In a highly influential branch of AI known as 'natural language processing,' problems can arise from the 'text corpus'—the source material the algorithm uses to learn about the relationships between different words.
The problem of bias in machine learning is likely to become more significant as the technology spreads to critical areas like medicine and law, and as more people without a deep technical understanding are tasked with deploying it.
'If any major military power pushes ahead with the AI weapon development, a global arms race is virtually inevitable, and the endpoint of this technological trajectory is obvious: autonomous weapons will become the Kalashnikovs of tomorrow', says the petition, which includes Skype co-founder Jaan Tallinn and MIT professor of linguistics Noam Chomsky as additional supporters against AI weaponry.
Regarding the potential for smarter-than-human systems to be employed militarily, the Open Philanthropy Project writes that these scenarios 'seem potentially as important as the risks related to loss of control', but that research organizations investigating AI's long-run social impact have spent relatively little time on this concern: 'this class of scenarios has not been a major focus for the organizations that have been most active in this space, such as the Machine Intelligence Research Institute (MIRI) and the Future of Humanity Institute (FHI), and there seems to have been less analysis and debate regarding them'.
To account for the nature of these agents, it has been suggested to consider certain philosophical ideas, like the standard characterizations of agency, rational agency, moral agency, and artificial agency, which are related to the concept of AMAs.
In 2009, during an experiment at the Laboratory of Intelligent Systems in the Ecole Polytechnique Fédérale of Lausanne in Switzerland, robots that were programmed to cooperate with each other (in searching out a beneficial resource and avoiding a poisonous one) eventually learned to lie to each other in an attempt to hoard the beneficial resource.
In 2009, academics and technical experts attended a conference organized by the Association for the Advancement of Artificial Intelligence to discuss the potential impact of robots and computers and the impact of the hypothetical possibility that they could become self-sufficient and able to make their own decisions.
They noted that some machines have acquired various forms of semi-autonomy, including being able to find power sources on their own and being able to independently choose targets to attack with weapons.
In a paper on the acquisition of moral values by robots, Nayef Al-Rodhan mentions the case of neuromorphic chips, which aim to process information similarly to humans, nonlinearly and with millions of interconnected artificial neurons.
Inevitably, this raises the question of the environment in which such robots would learn about the world and whose morality they would inherit - or if they end up developing human 'weaknesses' as well: selfishness, a pro-survival attitude, hesitation etc.
Wendell Wallach and Colin Allen conclude that attempts to teach robots right from wrong will likely advance understanding of human ethics by motivating humans to address gaps in modern normative theory and by providing a platform for experimental investigation.
Nick Bostrom and Eliezer Yudkowsky have argued for decision trees (such as ID3) over neural networks and genetic algorithms on the grounds that decision trees obey modern social norms of transparency and predictability (e.g.
while Chris Santos-Lang argued in the opposite direction on the grounds that the norms of any age must be allowed to change and that natural failure to fully satisfy these particular norms has been essential in making humans less vulnerable to criminal 'hackers'.
Concerns cited ranged from how AI is used in surveillance and in spreading fake content online (known as deepfakes when they include doctored video images and audio generated with help from AI) to cyberattacks, infringements on data privacy, hiring bias, autonomous vehicles, and drones that don’t require a human controller.
Many researchers have argued that, by way of an 'intelligence explosion' sometime in the 21st century, a self-improving AI could become so vastly more powerful than humans that we would not be able to stop it from achieving its goals. In
However, instead of overwhelming the human race and leading to our destruction, Bostrom has also asserted that super-intelligence can help us solve many difficult problems such as disease, poverty, and environmental destruction, and could help us to “enhance” ourselves.
Unless moral philosophy provides us with a flawless ethical theory, an AI's utility function could allow for many potentially harmful scenarios that conform with a given ethical framework but not 'common sense'.
Amazon, Google, Facebook, IBM, and Microsoft have established a non-profit partnership to formulate best practices on artificial intelligence technologies, advance the public's understanding, and to serve as a platform about artificial intelligence.
They stated: 'This partnership on AI will conduct research, organize discussions, provide thought leadership, consult with relevant third parties, respond to questions from the public and media, and create educational material that advance the understanding of AI technologies including machine perception, learning, and automated reasoning.'
The same idea can be found in the Emergency Medical Hologram of Starship Voyager, which is an apparently sentient copy of a reduced subset of the consciousness of its creator, Dr. Zimmerman, who, for the best motives, has created the system to give medical assistance in case of emergencies.
This event caused an ethical schism between those who felt bestowing organic rights upon the newly sentient Geth was appropriate and those who continued to see them as disposable machinery and fought to destroy them.
AI in Banking – An Analysis of America’s 7 Top Banks
While tech giants tend to hog the limelight on the cutting-edge of technology, AI in banking and other facets of the financial sector is showing signs of interest and adoption even among the banking incumbents.
In this article we set out to study the AI applications and innovations at the top banks, helping you to answer the following questions: Through quotes from company executives and data from our AI in Banking Vendor Scorecard and Capability report (interested readers can download the Executive Summary Brief), this article serves to present a concise look at the implementation of AI at seven of America’s top commercial banks by revenue.
First, we’ll provide some insights about the state of AI in banking we discovered through exploring our data and interviews with banking executives Readers with a broader interest in AI’s applications across the financial sector may be interested in reading our article Artificial Intelligence in Finance, which covers a wider array of applications beyond the top US banks.
The full breakdown of AI vendor product offerings by function is provided in the graph below: We should note that banks are likely understating their use of AI for other use-cases, and the banking experts we interviewed for our report and our AI in Banking podcast all agree that banks are investing in AI for compliance and risk monitoring more than any other business area.
At the same time, banks are likely overstating their use of AI for customer service applications, including chatbots because: It will be a while before the technology has advanced enough for chatbots to generate natural language and hold conversations with customers more often than they’re routing customers to customer support agents.
Lastly, our research found a number of top banks referring to AI as an “augmenting” force for their employees, not a “replacement.” To us, this seems to be a necessary move of the communications department, but a disingenuous way to describe AI’s potential impact on jobs, which will most likely involve both “augmenting” and replacing human beings outright.
Unlike many modern tech giants, old banks often have thousands of employees performing mundane paperwork and “legacy” processes, many of which may require complete elimination once machines can replace humans at the desk.
The Emerging Opportunities Engine, introduced in 2015 and discussed in a letter to shareholders, purportedly uses machine learning and natural language processing to help “identify clients best positioned for follow-on equity offerings.” The technology has proven successful in Equity Capital Markets and the company stated their intentions to expand it to other areas, including Debt Capital Markets, but it’s unclear if this has happened yet.
Wells Fargo hasn’t publicized many artificial intelligence initiatives, but Steve Ellis, head of the bank’s Innovation Group, seemed eager to leverage AI in a 2017 press release for a chatbot pilot: AI technology allows us to take an experience that would have required our customers to navigate through several pages on our website, and turn it into a simple conversation in a chat environment.
Katherine McGee, Head of Digital Product Management at Wells Fargo, elaborated on one of these prompts in an email to Bank Innovation: If a customer receives an incoming deposit which is not in their usual pattern of transactions and is not needed to meet their normal expenses or scheduled payments, we can highlight the deposit and suggest the customer save the funds.
Consistent with our high-tech, high-touch strategy, we’ll continue evolving our best-in-class digital banking capabilities, including Erica, to provide clients relevant, timely guidance and help make managing their finances easier.
Although the demo video below shows how Feedzai’s software works for eCommerce companies (and is admittedly a little corny), the principles it describes can certainly apply to banks: Feedzai’s software will purportedly monitor customer payment behavior for deviations from that customer’s normal payment activity.
The software is explained further in the video below and a promotional video can be found on US Bank’s website: According to US Bank, using Expense Wizard, a hiring manager can provide a virtual card to a candidate via the app, setting a card limit via US Bank.
PNC invested $1.2.billion over five years, according to its 2016 annual report, into modernizing its “core infrastructure and build[ing] out key technological and operational capabilities,” with the objective of faster, more secure and more stable operations and services.
Still in the early stages of its tech strategy, the company’s initial focus has been on the consolidation of its data centers and a major shift to an “internal cloud environment.” We can presume that the company’s infrastructure upgrades will help them leverage data and implement artificial intelligence and machine learning.
Examples include “data requests from external auditors” and “funds transfer bots” which help “correct formatting and data mistakes in requests for dollar funds transfers.” The video below provides an explanation for how Blue Prism’s AI software works: Former Senior Executive Vice President and Global Head of Client Service Delivery at BNY Mellon Corp, Doug Shulman, said this about the bank’s investment in RPA: If you think about smart automation, robotics is a piece, workflow is a piece, and we’re combining smart forms, optical character recognition, workflow and robotics to get momentum around automating tasks.
Expanding our knowledge, finding new answers
We wanted to try a new interdisciplinary approach to accelerate the field – bringing together new ideas and advances in machine learning, neuroscience, engineering, mathematics, simulation and computing infrastructure - as well as a new way of organising scientific endeavour.
One of our programs learned how to play 49 different Atari games from scratch just from seeing the pixels and score on the screen, and our AlphaGo program was the first to beat a professional player at the ancient game of Go, a feat experts described as a decade ahead of its time.
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- On Monday, November 18, 2019
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