Robots can be programmed to perform all sorts of tasks. They can do legal research, drive themselves, and sell you insurance. But can you program a robot to compute ethics? Mike Loukides from O’Reilly Media is less optimistic about this possibility.
What do you think: is it possible to imagine an AI that can compute ethics?
Mike Loukides, Vice President of Content Strategy for O’Reilly Media, asks ‘is it possible to imagine an AI that can compute ethics?’ He says that if ‘living a good life’ isn’t a difficult optimisation problem, he doesn’t know what is. He doesn’t see how ethics based on a priori considerations, like Aristotle’s ideas of duty or virtue, the Ten Commandments, or Asimov’s laws,
could be computed.
These are all external inputs to the system, whether handed down on stone tablets or learned and handed down through many generations of human experience. He doubts that an AI could derive the idea that it must not harm a human—if we want AIs to behave according to anyone’s laws, they’ll have to be built into the system, including systems that have the ability to write their own code.
Researchers have used machine learning on the Stampede supercomputer to model the cellular control network that determines how tadpoles develop. Using that model, they reverse-engineered a drug intervention that created tadpoles with a form of mixed pigmentation never before seen in nature.
The utility of these methods is their ability to find novel regulatory interactions and even novel necessary regulatory genes. These methods are indeed becoming indispensable for understanding the complex coordination of signals necessary to develop and maintain correct body shapes and organs. Moreover, such methods are required in order to develop interventions to make rational changes to complex anatomy and physiology, in the context of regenerative medicine and systems-level diseases such as cancer.
Facebook has started rolling out its third-party fact-checking tool in the fight against fake news, alerting users to “disputed content”. The site announced in December it would be partnering with independent fact-checkers to crack down on the spread of misinformation on its platform.
The tool was first observed by Facebook users attempting to link to a story that falsely claimed hundreds of thousands of Irish people were brought to the US as slaves.
In one of the largest investments in the legal tech industry to date, Casetext, which provides AI-based legal research technology for lawyers, has closed on a $12 million Series B funding round.
Casetext’s CARA uses AI and natural-language technologies to automate key legal research tasks, arming lawyers with the highest quality research possible, ultimately allowing firms to spend time on higher-value, billable work—and not miss key precedents or decisions. Because CARA is powered by the Casetext research database, users have access to a full library of federal and state law, annotated by expert analysis from leading attorneys and law firms.
Seed retailers are using AI products to churn through terabytes of precision agricultural data to create the best corn crops, while pest control companies are using AI-based image-recognition technology to identify and treat various types of bugs and vermin. Such markedly different scenarios underscore how AI has evolved from science fiction to practical solutions that can potentially help companies get a leg up on their competition.
Beck’s Hybrids, for example, is using an AI product to analyse large amounts of data to determine which corn breeds and which conditions will produce the highest yields. The company’s geneticists need to know how sun light, rain, location, terrain and could affect growth and profits for the more than 30,000 different types of seeds it offers.
Current state-of-the-art sports statistics compare players and teams to league average performance. For example, metrics such as “Wins-above-Replacement” (WAR) in baseball, “Expected Point Value” (EPV) in basketball and “Expected Goal Value” (EGV) in football and hockey are now commonplace in performance analysis. Such measures allow us to answer the question “how does this player or team compare to the league average?” Even “personalised metrics” which can answer how a “player’s or
team’s current performance compares to its expected performance” have been used to better analyse and improve prediction of future outcomes.
Motivated by the original “ghosting” work, Disney researchers showcase an automatic “data-driven ghosting” method using advanced machine learning methodologies applied to a season’s worth of tracking data from a recent professional league in football.
CBInsights recently put out a blogpost which discusses the many effects which driver-less cars will have, beyond the automotive industry itself. The author argues that the impact will be far-reaching and lists the industries which will be effected, including: insurance, hotels, airlines, and real estate.
Palo Alto-based startup Next Insurance has launched an insurance chat bot to enable small businesses to quote and buy insurance via Facebook Messenger. It partnered with enterprise-focused chat bot developer SmallTalk to provide tailored insurance policies for small businesses via a social channel.
Guy Goldstein, co-founder and CEO of Next, said: “70 percent of our customers are buying insurance on their phones. Enabling customers to buy insurance through a chatbot on Facebook Messenger brings simplicity, transparency, and easy access. We’re making sure that insurance is working for the small business owner and not the reverse.”
Dean L., on HackerNoon, being inspired by a mosaic at the MoMA in San Francisco, decided to take a crack at making his own art using algorithms. He walks the reader through his attempts to do so, first by using deep neural networks (which took ages!), then by concocting some home-grown algorithms. He produced some really cool art and walks you through the steps so that you can too.